{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial Networks with MedNIST Dataset\n",
    "\n",
    "This notebook illustrates the use of MONAI for training a network to generate images from a random input tensor. A simple GAN is employed to do with a separate Generator and Discriminator networks.\n",
    "\n",
    "This will go through the steps of:\n",
    "* Loading the data from a remote source\n",
    "* Constructing a dataset from this data and transforms\n",
    "* Defining the networks\n",
    "* Training and evaluation\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/mednist_GAN_tutorial.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU monai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.2.0\n",
      "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
      "Numpy version: 1.18.1\n",
      "Pytorch version: 1.5.0a0+8f84ded\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.3.0\n",
      "Nibabel version: 3.1.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 7.2.0\n",
      "Tensorboard version: 2.1.0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import io\n",
    "import tarfile\n",
    "import urllib\n",
    "\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset\n",
    "from monai.networks import normal_init\n",
    "from monai.networks.nets import Discriminator, Generator\n",
    "from monai.transforms import (\n",
    "    AddChannel,\n",
    "    Compose,\n",
    "    RandFlip,\n",
    "    RandRotate,\n",
    "    RandZoom,\n",
    "    ScaleIntensity,\n",
    "    ToTensor,\n",
    "    Transform,\n",
    ")\n",
    "from monai.utils import progress_bar, set_determinism\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define training variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_train_interval = 1\n",
    "disc_train_steps = 5\n",
    "batch_size = 300\n",
    "latent_size = 64\n",
    "num_epochs = 50\n",
    "real_label = 1\n",
    "gen_label = 0\n",
    "learning_rate = 2e-4\n",
    "betas = (0.5, 0.999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),\n",
    "[the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4),\n",
    "and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)\n",
    "under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).\n",
    "\n",
    "\n",
    "If you use the MedNIST dataset, please acknowledge the source, e.g.  \n",
    "https://github.com/Project-MONAI/MONAI/blob/master/examples/notebooks/mednist_tutorial.ipynb."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method for loading the data from the remote source differs here to demonstrate how to download and read from a tar file without using the filesystem, and because we only want the images of hand X-rays. This isn't a classification example so the category data isn't needed, so we'll download the tarball, open it using the standard library, and recall all of the file names for hands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "resource = \"https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1\"\n",
    "\n",
    "remote_file = urllib.request.urlopen(resource)\n",
    "dat = io.BytesIO(remote_file.read())\n",
    "\n",
    "tar = tarfile.open(\"MedNIST.tar.gz\", fileobj=dat)\n",
    "hands = [n for n in tar.getnames() if \"Hand\" in n and \".jpeg\" in n]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the actual image data from the tarfile, we define a transform type to do this using Matplotlib. This is used with other transforms for preparing the data followed by randomized augmentation transforms. The `CacheDataset` class is used here to cache all of the prepared images from the tarball, so we will have in memory all of the prepared images ready to be augmented with randomized rotation, flip, and zoom operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class LoadTarJpeg(Transform):\n",
    "    def __call__(self, data):\n",
    "        return plt.imread(tar.extractfile(data))\n",
    "\n",
    "\n",
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadTarJpeg(),\n",
    "        AddChannel(),\n",
    "        ScaleIntensity(),\n",
    "        RandRotate(range_x=15, prob=0.5, keep_size=True),\n",
    "        RandFlip(spatial_axis=0, prob=0.5),\n",
    "        RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),\n",
    "        ToTensor(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "train_ds = CacheDataset(hands, train_transforms)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_ds, batch_size=batch_size, shuffle=True, num_workers=10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define out generator and discriminator networks. The parameters are carefully chosen to suit the image size of `(1, 64, 64)` as loaded from the tar file. Input images to the discriminator are downsampled four times to produce very small images which are flattened and passed as input to a fully-connected layer. The input latent vectors to the generator are passed through a fully-connected layer to produce an output of shape `(64, 8, 8)`, this is then upsampled three times to produce a final output which is the same shape as the real images. The networks are initialized with a normalization scheme to improve results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")\n",
    "disc_net = Discriminator(\n",
    "    in_shape=(1, 64, 64),\n",
    "    channels=(8, 16, 32, 64, 1),\n",
    "    strides=(2, 2, 2, 2, 1),\n",
    "    num_res_units=1,\n",
    "    kernel_size=5,\n",
    ").to(device)\n",
    "\n",
    "\n",
    "gen_net = Generator(\n",
    "    latent_shape=latent_size, start_shape=(64, 8, 8), channels=[32, 16, 8, 1], strides=[2, 2, 2, 1],\n",
    ")\n",
    "\n",
    "# initialize both networks\n",
    "disc_net.apply(normal_init)\n",
    "gen_net.apply(normal_init)\n",
    "\n",
    "# input images are scaled to [0,1] so enforce the same of generated outputs\n",
    "gen_net.conv.add_module(\"activation\", torch.nn.Sigmoid())\n",
    "gen_net = gen_net.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define the loss functions to use with helper functions to wrap the loss calculation process for the generator and the discriminator. We also define our optimizers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_loss = torch.nn.BCELoss()\n",
    "gen_loss = torch.nn.BCELoss()\n",
    "\n",
    "disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas)\n",
    "gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas)\n",
    "\n",
    "\n",
    "def discriminator_loss(gen_images, real_images):\n",
    "    \"\"\"\n",
    "    The discriminator loss if calculated by comparing its\n",
    "    prediction for real and generated images.\n",
    "\n",
    "    \"\"\"\n",
    "    real = real_images.new_full((real_images.shape[0], 1), real_label)\n",
    "    gen = gen_images.new_full((gen_images.shape[0], 1), gen_label)\n",
    "\n",
    "    realloss = disc_loss(disc_net(real_images), real)\n",
    "    genloss = disc_loss(disc_net(gen_images.detach()), gen)\n",
    "\n",
    "    return (realloss + genloss) / 2\n",
    "\n",
    "\n",
    "def generator_loss(input):\n",
    "    \"\"\"\n",
    "    The generator loss is calculated by determining how well\n",
    "    the discriminator was fooled by the generated images.\n",
    "\n",
    "    \"\"\"\n",
    "    output = disc_net(input)\n",
    "    cats = output.new_full(output.shape, real_label)\n",
    "    return gen_loss(output, cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now train by iterating over the dataset for a number of epochs. At certain after the generator training stage for each batch, the discriminator is trained for a number of steps on the same real and generated images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "epoch_loss_values = [(0, 0)]\n",
    "gen_step_loss = []\n",
    "disc_step_loss = []\n",
    "step = 0\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    gen_net.train()\n",
    "    disc_net.train()\n",
    "    epoch_loss = 0\n",
    "\n",
    "    for i, batch_data in enumerate(train_loader):\n",
    "        progress_bar(\n",
    "            i, len(train_loader), f\"epoch {epoch + 1}, avg loss: {epoch_loss_values[-1][1]:.4f}\",\n",
    "        )\n",
    "        real_images = batch_data.to(device)\n",
    "        latent = torch.randn(real_images.shape[0], latent_size).to(device)\n",
    "\n",
    "        gen_opt.zero_grad()\n",
    "        gen_images = gen_net(latent)\n",
    "        loss = generator_loss(gen_images)\n",
    "        loss.backward()\n",
    "        gen_opt.step()\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "        gen_step_loss.append((step, loss.item()))\n",
    "\n",
    "        if step % disc_train_interval == 0:\n",
    "            disc_total_loss = 0\n",
    "\n",
    "            for _ in range(disc_train_steps):\n",
    "                disc_opt.zero_grad()\n",
    "                dloss = discriminator_loss(gen_images, real_images)\n",
    "                dloss.backward()\n",
    "                disc_opt.step()\n",
    "                disc_total_loss += dloss.item()\n",
    "\n",
    "            disc_step_loss.append((step, disc_total_loss / disc_train_steps))\n",
    "\n",
    "        step += 1\n",
    "\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append((step, epoch_loss))\n",
    "\n",
    "    IPython.display.clear_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The separate loss values for the generator and discriminator can be graphed together. These should reach an equilibrium as the generator's ability to fool the discriminator balances with that networks ability to discriminate accurately between real and fake images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f89162aa780>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.semilogy(*zip(*gen_step_loss), label=\"Generator Loss\")\n",
    "plt.semilogy(*zip(*disc_step_loss), label=\"Discriminator Loss\")\n",
    "plt.grid(True, \"both\", \"both\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we show a few randomly generated images. Hopefully most images will have four fingers and a thumb as expected (assuming polydactyl examples were not present in large numbers in the dataset). This demonstrative notebook doesn't train the networks for long, training beyond the default 50 epochs should improve results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jJ7M0kt/KMN0tX2XoWntEbRngRufrpqJGhTD9k1F9/I5d03bcZ7MZut0u6vU6zs7O8E//9E94/fo1rq6ucHl5iW6361KH2T+WHFgsFg678Pm+TUC9R/lkr7PNugmgO/mbHEefY1/JOjcs2XHWZyhOCz1br/uwhxr16XTa8YCbCmqwa198jgPFiaH++jCdYneLYbSPOu+JXX3zztrjm6DhcBhxXBLbK4ZiYAQAt7HNPpOvKu/U7mPf+HcymXR8GQ6HLi2fayGVSqHf77tUL36H78lkMshmszg7O8NkMnE4hnzWtau8isViDlNx3fPZDFJRW0rTscgb2kRqm6jNoo4pdc6ojFMbO0QrU59IauzpBLTCxKfgQ4CPz1BBpAtN3+PbGdX8Sxp5jEpRUs82B4WTR4W3CnEb4qiK3aaE8f+6IG0/VHDogCloYCSKzbH7UFJv43w+dylJACKOKxUUNCI4eXyRBvqjTqd+v49ut+tq03AnYTgc3jJCVMjp7ikNL1+4sI5HCHjq/1QhheYnHVVc2JzbVOJ8Fg1d7nBSkCkw1fdvipRPVgGo4PYpGBu1pc/RexQwqgJXw1uFr75fhS+/w5QHjo+mU+h4KnACorv6VobwXfoda0io0rDt0+v2Hgu8rLzZBOk4rvrM9vnkSgio2nFROUQgx6gORsyEwBPv1+gL1stoNBpLQYrtc+gdPmBneWDHRsdF5YG22/eMTdJ8Po/IavaPtV7evHmDer2OTqeDZrPpeH18fIxarYZarYanT59GaqtRH9joGe0/87fpVAPeyfCvvvrKAY9QSg/HU52qABzoofM0xDPlNeVAvV7Hs2fP8PbtW3zzzTfOSUMHFXHDaDTCmzdv8OzZM5TLZdcGgiI6aHq9HtrtNl68eIFer+dkr6Z+bpK0j9a49c3tZff77rHXrUERei6vkUfctBiNRri+vnbzRNPOqJMoEzUKiXpV9eZd+uFr7zJnjX5H5+8y/rwPcaMOuDHEWI9PNyHUoWw3Umxf1hkbykRiASUaKbZeh92R9snpZe/2/R2iu9ynOH7Z3FX9u2l5arGcyho7Nj5e+Qxz2+5lOoGyVSNqyBOOI6NqQk4t1YfJZNI5R1njkHLZ8pBzQ9uo7+dvlel2bek9tm92/d0XvvHNn2WOmXWeo30CbjuNQ9jC8o66L5VKuQ0TblypbUK968Nhy9q4bH6FeKIbEPq8ZRhQbWFdL5skvk8jARVn2zVKW832yadv2Ga7psmDyWTibAYNBlCnlWaFaDQz1814PEav13Pfy+VytzaYuR75Y/GFOlF1LoV8GnZdUwfourTrza7D946osYpt2aSwi3SVMFgWRRNqi1V2NNjonKHTwQ4KJxvz/Ale1OjUAbCTTgdAvWzaD7bf10ftvzpolG8E/vTgb5JUuXAi07Nsd14BuB04LgbyxJcnqLzhDyOEVABqkcvF4sYBxOdaI4zCgfzTH77XOtd4XfkaUny8h8T2AYikdtC5x+9SAdOw0d10S5sEM5bf2n51pPgEoXXI8DlWAClvfEbeMkOGjh7An3+ped+cB2pQagSLgtwQsAqBbr3XpxDs/T7Byeub3nHSdixT6iGFbZ9h/7aGkG+uW4Od0WzrRFHwh3VN1jE6V/HB9zv0LN+6tbykklyHhx9CuhPLCLDpdIrz83NcXFzg66+/xuvXr3F9fY1Wq4VY7F2E5pMnT3BwcICjoyPk83kXXVMsFt14sQgl5ybrGagM29nZcU62yWSCo6Mj9Ho9NBoNV0fIEgGKOmoWi0VkLqwiK1NHoxE6nY6bG3Zt8ztXV1d49uwZ/v3f/93toMViMWSzWacz9HsaMQcgcm1TFJIt6/x/mVHrA6Ghv0P6iOuUu4uMQJrNZmi325G0Uo6fOifIU92UUuDs66fPWWHXlm23/vaRb31ukmy6u+p6/oTqNmlffO22n62xxJ1cRkDz3fP5PGL0hRynyr+QPljVpvchnzMjRDq2NN7uwzjkuyyWVJwC+OuyKD7x9cunA0JzmnqQ64b4ReePfY/9m048OmqKxaJbm6E2WueUz+FijV7lid7rm8/EM/r/+9KNJCvPtI2rKIRv7BpY9TzlCWVpoVDA3t6e03k7OzvodDoA/IfG6PsBf9qKGuO2/T5s5utnaL7q3yrffXJjE0Q5xqgRDZJQ3U+yNjnbE9oYWOUo53vz+XzEQckIW3WoWjlPe20wGGA+n7vxnc/nEdyja9o61FWHkOzcszrHyqllOlGdbcq/ZTJ1rdQnbZRPMfPFKgzsZxKBhRpovIcGrxrpShq+yu/oc/L5PCaTCX7wgx9EiuYyjKnT6bhaKTp5stmseyZ3F/mZg8/3W4FhJ5jyRK/ze7yuQpsgnAbtphceQ9k0dF53hLLZrEuJogHGIqIUSBxjHRedDwyr5gJnPYvHjx9jOBy6xUNFWK/XXXG1ZrPpdp57vZ4TmnwXjQuN2uG4q/PHLiydd1x4BLuch3RaTadTXF1doVAoYGdnB6PRyLWl0+k4R8R4PEapVHLeXu7eAbeN/vtIm6HwUdJ5ZUODuX6tR5jPswAIuK08NE1J6x3wexqNw/Gn8Z/P5911RrOph17XBa/piSTaDnWOWkGpn21ffffoc33Eoqj3RVYe+ECN0iqDyKcgKBfJfzXWuEufSCTQ6/Xcd7Q4aSwWc05XlX109CSTSRdRwvlhHbm2j8uMVL3PjrUqN50ryk/fZw353hQx0oPO/6urKzQaDSSTSbx8+RJv3rzBy5cvcX5+jmazieFw6Hh2enqKg4MDnJycIB6Po1qtRsD8bDZzNQwor7U/TOfiSSV0EtnCxqpH+JtjQyOT48/n0hngM9gt+UDLsvutLCbxJCsfcU5oCvamKQSs1/1eaD2SfM4XftY1Q1KHaD6fdyljjx8/xkcffYRPP/0UDx8+RLlcRrFYxGKxcCc38icWi2E4HCKfz7vNKbvjZ9uva9EaAD5+2GtW1upvq1s2OY6aMrNYLHB5eYler+dOTVsHT/mMfHVk6prQUzIfP37sUgiBG+dts9lEq9VyTjXy3bf5sapdljbBO98zfHrR4oH7WoOAf8PFNyetc4L36bpRm4J8ts47Ppd4cjgcotVqodlsOmNeN5psLS99t/2scyYejyOXy2F3d9fhGuph3st2WGxmn0lZrbjb8mnZZ7vZuWmy4xfCZUqr5rjVXby2Cs/Z67FYDIVCAbu7u9jf38fJyUkkW4D8YaaBrz8knWcaFAAsd0KE1vsym9J3L++nXXkfdgZ/a9qVTX1imzX1SVOiLAZRe1f7w7Wp65UbtslkEv1+P+Ic06jJvb09l8LNdPxms4nBYBDhjUbe8L2M4lV8y/ZplCav6wFHel3xqo61YlddA/zhd5alPJGW3qGKXa/pb8t83Qlbx4DWzzoRFKjb9+u7+A6mO+nCpkJVbxyfrYbceDyOCHjddWRkgC2OyL7ojy+ihhNcF58aHHRa0Xilg2CTpKCA4J8745xMo9HICSnNc9VoF/JDDfLFYuEW1Hz+Lhy72+26I6vb7bY7+cmeBMJxoFHCxUNlonUnKDTszqD1hFohZ+erT5BzLuiOtc5rjTYYDofIZrOR+UmnINtCZ9AmI6NCa8Ya6D5QrHN9GegK/c9+1h+OiRoeFJCaTqHCTYWz5bdNjWIffV57O97aTl+Ule2j/Z/KGnVubZLsjqFvrJT/vnbq3xZM2/+Fxo7Hbp+dnbncWwAol8sAborFch32ej23rq3jPtQun5L29dm2TXWE3qNzOzQfgBvnDuf9psFpr9fD5eUlXrx4gUQigcvLS9TrdSwWC5yfn+P6+hrX19fodrvOWGSbu92uA/3fffcdut2uc9Rwp6jdbrsdecpkrqdyuYzZbIZMJuPkYyqVQqlUcsfJ+nhLPpEvmoNNR739rm/urzIu70o+mf190ao1tu4zlq0Bku+zz+DSe6h/tWg1EI3mou7Vekdct9RBDAXnLqOvn6G+r+IJ32l/6/91XYfm1fuSddj6nJu2reuQT27GYjGHI4mXSqUS8vm8q91C/cXaf3a3lqSYykcWW67jDN0kheb0fbYhpPtC7w3pNt+mk9U/Vu/T+NM0Ck331Q3LVbiA48ro60ql4mpuMDXUOhHXwRohPWb54HOwkSeKo9Ug/T4oJCuXOWuU5/ZwipBtaj+rLM1kMqjVai6iBoCrD8oNFf2+HR/F2MSx/I6vTdrHZfPZ4l3fPUq6Sb1px5tv3vBvu858uFU3cS0GDOESHyZmpoyNhOG7fRhQ8R6fr89h+iJ5p3a/7Ze2EbgdqLFKBvjGReU+20H7cpnDbWXq0zoN8i0QX/i5nYCrPIdWmdkIAJtKw/dQSAJwjhrWy7CDslgsIv+jx4653loQiJPQev7U8aLGiw6UbxA0FFEHbdOnWygwpHBmAWH2sd/vO4BnIyjUYaXOGfJeHTBMdRoOh85byc/0vOpug/Usko88khZAxAlix93ulGh/lZbNYV34Osf4PwVbGtXF8WMUgh7FuWlAugp82P5YQ3mVQvOBntC7VaBZpyfnDMeQUQI63nq/bfey68sMAJLP8FkFZGyfQ+3YBNm54Rsj3/9XjfWqd5Ioa7iLqIVblwFBpq3pSRi6w+ebU9aAW3av5U1oXvrG2hoxdhw3DWYGgwGur6/d3L68vHQFf1utFrrdrkv71Nxnysper4dUKoWLiwtMp1Pkcjknf7kjT/nc7XYdSE2n006/5fN5p9x3dnaQz+ddlEwISFBeapTUfD6/FY3xPhRymN7le/bdqidCDoYPpft4pn2+NbrXMVZ4HyNeWctIsRCdMKqrueuvqaZM96aeXvZu37pbt4/a9vvmK0kNaEabFYtF51xm2qDWqFmmD0Ny1n4vFou5opXJZBLFYhHAO6Ov3W4jFot5i+IrLZv77Jv2TzHPpmidcbNzd9PYBljtNPWN2Tp63n5PsTB/K7bVyA06b3wp//qOUH/4bNoQlP10DKzjfNN3WHtMrysPfLYGryt2u49xtPQ++kRxnTppGOXMNaCRGOo8Cc0Lbk7n83kXFQ8A7XYbnU4nEtngm28+TL0MQ+p15ccqzB2Syfa++8A2y95v8VkIr4XuDznB7CayfmbmB8eedpqm++rplSFnB1MR8/l85J2MyKQTxvLTx4d15ICVM/o9laPsjw1IsbQ65sYQjX02gDVhFEyxAQTydhFpqJmGE+ozqXh3dnZcaKnuUHBHg0YGFeNsNkOxWEQul3PgttvtRsJR6RDhZ7YnkUigUCg4D3gikXARIbFYzEW6WNCojhvrFVUnDEkNVxWeVAi2GPKHkk4kAnKm/dA5wR1zm8ZDoMijfLm7wDFfLBYuhJ6gn3UXyuWy20HgLgKVYrfbxWAwwGg0QjKZdDvImUzGpUSdn59HFhB/W+Di86yHQJAuMmsAsA2FQsEteIJd7l5zXqfTaceXeDyO09NTAHCnvWx6p0IXufZJFz6v814F9dbZGRL+9u+QV1y9+WqIkq8UpJVKxTkGmQ6lbbR90XBOq3Q5JznX9D59jvKL7eF39f7Qdf5932SVwCqHk34OgcQQQOC8pVH/2Wef4Q//8A/x05/+FIeHhyiVSiiVSq7uiVWqh4eHTm7WajVnBHW73Ui0YqhdPsVl26kK3X72zXdes2On7+D/1qm/si6dnZ1hOBzi4uIC4/EYzWbTOWa0vpICSe0nT5K7urpyudiMnKGjptfrodfr4erqys3zTCaDZrOJg4MDTCYTHB8fu51aGqi5XC6SvmvnAA3Jer2OUqmETCbjTiHhHFk2930ySHWZlckWfOh32C8aQdTD/I7WAaPs2CRtykgJ9VE/L1uv9n7Kz1KphIODAzx+/BgPHjzAyckJHjx4gEql4sbY4inKZR4H3G630Ww2IzXYQkaI7cu6/NHx5/yxOsPuXG6K+M5YLIaTkxP8zd/8Df7iL/4CzWYTv/71r/Htt9+i1Wrh/Pwc7XbbnQoSqgXo29W2faHeazabyOfzaLfbKJfLEWOSjnDfLjtll6ZO6Hrh/3miHNMA1PGzaT761rSVo2wv9f8myaZxhJxGvjarDFHHVmiO6/84VnRoaqShtsVuSvGHjnG+Qx3h19fXOD09xS9+8Qt88803ePv2LVqtlnPghwxCKyd8GIR2JLgAACAASURBVMXaGj4j0t6v11mEe5NziOR7p51PpGVyR22ybDaLarXqdKs6YLkJrCUkbHt4jRiGRr7aMvy/b1NA269OPcWi1gbku0NOFx/G8/HJh5NX8fVDyLeeAETwjD3pScnXTt9aVJznu4enOB8eHjp7PhaLod/vO7v06dOnLm282WxiMpm4+UA8s7e3h+PjY+zu7mJvbw+x2LvISE1PB97hMps1Y20E9s8efOQbI0190n6TZ+StypAQrUx90oZYpiq40u9YA8oOjgpY23F2jJOFp2Xwfua0LxaLCBgmxWI31aYZGZNOp5HL5ZxhbY/vItM0VLFQKLh+pFIpJ8xDThp+Vn4tU/b2u/ybeXObJIZv0vuoYEqV/2QyccKHIIMh8ZlMBrPZTcVt7jYQFPL7PIqbCpDFLbvdLgC4/F91eHAR7uzsOGODc6Hf72MwGLj2MK1I06CWGQT87BMq+p3ZbOZ2PGKxmNsZ1yLI/L4Wm2MfbIjepsGML2rAB0h8kTc+Yehbz/b59jMQdQSFgAZ5ks/nkcvlIgZWCJCoUtD0Op/zwicgQ33wKYMQKNB77wMMA7cdX7a9tl2h94cU/jKinKUzdjqdurWVTqddvSY11nW3iuCmUqmg3W6j2+06wBfql6//oX6E+KK/ta86h0OK7j52nebzucuBZi0uRhKqA9Mnlzi3COZHo1GksDtTRxuNhjs5T1Oc+P5arYZWq+X0HHADwH0GCYnAlOk0THcaDofodDqRMH8fWUBpdbqOiW8e6JrUa6F7+eMD4L8v5NP39v8WUOt3fLrLt7vPe6kDCUr5nHK5HJkDnGsWo/n0xof0XX/rGFmnh4LbTVE+n3cbTnt7e875WK1W8fDhQyQSCVdjTgv8Wvm+jB++cV0sFu4Ey36/746VHY/HrhZiqD6OT6daLMw0R0brssYfnUybwhahOcj/sU2Kf0Lr9UPIh1ns/1fpFWL/kPwKGcxqL2gEOcfCV0NCx0zbp2uLOlNl+7J0FbuWLFmcZ6/75IveQ1xFh+59ydRl8ynkhNDPvI+RowcHB6hUKjg4OHCRgTwhkOss1Bd9Jx2zijNZm40RGb6++H6H+rSsP/aZIdL7Q/iWf9/HWlSyTgY713Re8XPo/mXY3ycPaRvSdqdc1Pt89rjFo3xWoVBwtaem0ymKxaI7vZRzyKYDhmyrkB2p/bUb2JYPlAPrZNCs7agJNcpOYN/k8RkV+j06B5RBXESpVAqFQgHATQqMRjf4gLgCBDoU6LBRwGOBPgtk8jQOHWwaJaECSfw75MVW3lge84fOIB4VvSni5NBJTvCr6QwMjU4kEuj3+27y8nehUHDFojTyhgCIJ4Dw+fP53B3N3el0nPeZQpFjk8/nXeFmpgDs7Oyg2+268WIblWdclNZY8y38EFBWXrB2D2tJ0KGn80CVHAB3+ooNveNzNk3LBPMqMOMzcEPAyPcc3QG0/FPSQt6MDrO74T7lpc4aICwY1wUXywxA+9mu3VDfPpRWGaW+di5T3Hc1uFQOaPoTjfdkMonFYnFLTlI2MlKuVCqh0+kglUrdOlUl1G9twzJe6H2h//uMHR+419+bIsq82WzmogK5G7sKPFFucYec8pcOL0aK0gDUTQXKl0wm44qcM+9eo83sbrBti0Y+cFxtjYZlho6VpcoX9nFdCskgnXs+QPb7TncxuizOUuzDMdFxIT8YZWR5RFnGe+l8W+aAW9a+u96v+pn9uS+DgkYwAFdLUJ2bXBtMG/PpPtv+EFneqdGtkRmaVrjquRw7xWgc+3K57KJqeAgE8A6LbnI9+IwvXxuB+3PULHOuhPS175rKjJCcsu3X1HaNjvAZgbZt+g59l/KJmHGZQ2EZrasv7f0hCq2BD6V1sIjFfcuIuq5YLKJWq+Hw8NCVU2Dh+8Vi4dLJ7vJctTvt5hT7Yj/reKs89fXbxwffXLmLA8c+d9O4xr7HYnyfzWBlhw/Th3DZsu9RB9pasbyma9RGLVp/gAZh0OHDAxjYPnVccoM4xN9153noe8oX6o1VdKdiwpyY2ojJZOKUCTtMRukJMew0GcRFpsdAa+PpAatWq/j0008BvMslvLy8RKfTQb1eR7vddoCFxgWNe/WIE+wo8wmU5/O5c8CwSGM6nXbH6XHAGRYVi8VcaJUOACeXDrLWELCCm5OPCsKmQ22S6DzhGBWLRRchQ4NDF8RicVODhhFNFJY02vL5vDPay+WyO2WhUqm4/jE9SAsJ88QS1sexOxZ02tBgZL2H//3f/3WGjYY9+iKq9Le9xv7xM50BrDVBpx5TOjTkXosskw+FQsGljvCUKKZ4bTqFjWSFqCpyX7/1PuvwsAYRr9vUCY7V/v4+ALgdPtY/UEGnEQPq3bZGpI4d38lx5xzSdeM7yWZZCCL757tu71cQrw6iTStDVXY+x5D97GuDCnx7j0+JAojwk7xeLBYYDAY4PT2NnHbASAuVf6zBkEwmUa/XXc7v+fm545ndGfC1cR1gouO+zGGgctIag5yv2Ww2chLBJoiRlfZ4bp3PIUMiFrtxOrdaLScjGLXJmiNcVwyTB+D0FQsRP3z4EOPxGMVi0Z1mSOOUDh8LAijz6egpFAouDYTyrN1u3zo5Sttv+6QUuu67J/RMICqrQ1GTvy9kxzd0z7IdX36PRnoul8PR0REePHiAhw8fIp/Pu6OgOVcWiwXy+bxbi4VCwUXTMI2YzlfWubmL4+iuffcZuBbsb5qurq6cTPvVr36Fer2Of/u3f3PrCHg355lOuM4pUJasfuXcJC7iyW06juu8Q/E1n6nHOh8fH7sxHY/HqNfraDQaaDab9+IwWTUHKPOAzetFFmO2J7zp75AjWGWcxXpqTKthp8/lyaNv377FgwcPkM1mXYoE76Oz07f5phiGm5+UsVdXV3j+/DkuLi7c/LNtCJHy2CcnFd/Ye7S//NtnbN+nsa90V8OWY0c7Qos6sxYV7QEbfWjxnq5XRqjpZoTFfT5nim7A2gLTgD+SYxVpWo014n28sdjJdwrZJkjlpuosLUNhiyj7ZGRIz/vu990DwKUQFgoFx3c9nEdrsbH2k601WigUUCwW3WELbBttO35PMZziSCA6B/hs39iRFL9YHvC7tpj4srFcWUxYG+IDJNawp8Gmk9A2RhcFhSzrlNhwId2V12KJ+i5daNPpFN1u17WL9zN/WJmtk05BNo3sbDbrDCrucgKI3K8CxQ4S+2rD4RXM2MHRyJFNEXdsye/JZOJyqekk0zw5OlH4mY4u5vJp6BiNOTpQBoNBJIqJKVc01MvlMqrVqhvbVCqF3d1dNxbVatW1Z29vD91uF61WC/F43B2Dy/BfBTrkO3nqu8ZxDxm4XKyaxqDCdz6fu7oczWYTzWYTuVwOhULhVkFWG0L3oeQT/lbRLAPhvjXsA0Sh93FOc2yZMgHcCEtdhwpY1CnKe1QJ2HZrDrB6uYHo0YH6W58TAji2X9p/Oy8sfzZFy3i+zjWrAHlt1ft0rdCJwZ9sNutkHAt4A3C70YvFIhKVWCgUUC6X0e/3Ua1WI84Klfm+dRjqm8/ICSn7EGDXdcA5pk7ETZHOXc2Nt/Ml1A/OZ8oMLSY3m707npvRNLquVH4zbW00GrnUUUafZjIZ9Hq9SJSqTwfzb55ip/Xm3oeWzesQ+UCaPm+V8fj7Qj6dYkF/qC8E3alUCvl8HuVyGQcHB3j06JHLreemh4bpUw8DNzKYGx80KJiCyu/a2hSr5P5dKbQmfUbiJkgdsAqqtVYUo9Po/AwZU3b++Qw2NfxUxzHihetoHYcQZTKfvVgsHFajzKWRysLl3MS8jzp469BdHFF3oVQq5cW+6+iQ0Ljp93SDzq5VRohXKhXs7u66jUyt9cXnUX/qe9QWshiJ6d/crFU8GeqL1Z36bt86CmE/+w69Z11n0V1IdcuH2DB8TiqVwtHRkZOBBwcHrnQF9WMsFrvF01CfdF2qTaYOFx9fre7UMeZ9qv99fPe1SbGrfs9iId8YUnbY6JFNkWJG2z6rO3zOLZ+dYXGb9pd/67OIRbkpb51xrIdKmciTDWm7cW7wtFJu8FMW0EehkW7Kz1XOGB9ZfOX7Ducc/29LPPjozsWEfQ3wCcBlHeFvjahIp9PueDRdCMylZ8dYZ0a9iWr8aUg5nQ/WcWB3V3WRa9qGKkct0qhGT6h/OhgqFNVrqwqEz9r07i+ASD2e0WiEdDodyXNmZA8nrhoIw+HQOWp4vCeBYC6XizjqaEQA0eJw1jPO8eT40iHG+xnexsLC7XYbr169ciCI4+ADDXZBrbu4AEScAaETnAjMqCjUucj3bVoBKq0DVFb1+S4GkAU1PPKeKS+LxY1nOgSYNGSRBgaFk64Hm4qhzl/rLbdCnu+z4x8CMMtInVqbHsdV4OquQDjkhNJrti8qE7kGCSR9xXABRGQoo+x4ekK73XZKMqTQQteW9SnUH9//fCBCAdkmx9HuMlknja9ttp3cPeYc14KwLLyuqVT8rjp59N00DFhTTDclQn3gc2kkcR7cJUXG9nHTwJ+/PxT83yeF1rHP0Nc1yN/qpGGExt7eHvb29lCtVh0Y1XQ14GZHlj/j8Tgyj9Qxy7Wu4+szCDdJVg/fB+lOd6FQwN7eHo6OjhwwZ9Sn8sRn2KyLEXRciTf1sIH3maPkjRrydCAQhy4WC2SzWYfHfNHt90WKV+/rnT5cru8HbusQYgYWVFcMrQaWNXx9zyfeZDS9YkxrzGmUjiXFu/pjdZHVx6TQmlxm7Pr45HvefcvPdea/HT/lgdqRdJ6xAOze3h5qtRoSiQQGgwFSqRQGgwG63W4wVdeSDx+ojbLqOWybnjYE3NQWfZ/1vwzjLPvM994HRiXZeaiY24c77fWQvLBzN7Rhq/epfUob3Y6l2gca9UL7hJsaamvo+tQf39zku0KY2+eQsfxch88+WstRo43X8D8Kjlwu54pRsnE0uIGb0ETer3nX1WrVnbTEEM/pdIparYZqtYoHDx7g+PgYOzs7LrqDqVaNRiOSAsM6KZrTXyqVMJ/Pkc1mUavV0Gw20el00Gg0HBAmxeNx5wHPZrORcNbBYBA55YcDYRe8Gp4U6txp0fs5QRSkW0/tpqjVajmlr6dmaXEttmM2m+Hq6iqSJ83vVKvVW1XxbfqBFgpOJBKRMUmlUs7onk6nri6NFoniMXnkM4sDsqgUK8BrXRw1rAH/ThjJJ0iUByElqgqffWDkUaFQcDU7Wq2Wc1ZtOoXNtj/UPu0TEFXWoUJZPuXOz8qPWq2G3d1dVCoVpNNpXF9fO0cf1x13ccvlMo6OjhCLvdvh5Kk1XIM6B6mcdUdLI3NarRaurq7czqiGO6vysIqE60qvW4+5T/F8n6DGAikdC4308d1jlaRetzuuuuNXLBZRqVRQq9UcQNVUCSuzrNOc8oSnRbGoroJR3zoMgW7liy8kmOQDt5Zn+iyt67EpUv1nHUHLFK4CE+7009GbyWQizs7Q81hLq16vY7G4KWbOkxFnsxmur6/daVR6JDMBDyMA6PzkaX4c01XAYRW4vAv5xlPlmIa1/64iCEJzK/S3jqEv0paf2T8W0Od6rFQq2N/fx+HhIfb29lAul3F6eoper4d8Pu+OZh8MBmg0Gm4eaLpyo9HA+fk5zs/P3WmVlMu+k4M2BfaXyeBNr0MSZdNPfvIT/NVf/RV+9rOfIZlM4uLiAufn5/jFL36B//mf/8GrV6/QarUcDvUZ/sBtnlg8wB9G33Y6HRweHjocQzxsnSn6LjUSbPq2bmyoQUEnQjabdXrwPsnKbbblPozDYrGI0WjkcKBdd9oG/k3n5ueffx45Efb8/Bz9fj8ipxUTaUoCr08mE3dKWq1Wc9iAY8MyC6wJZp0wfA515nT67nj4er2Os7MzVxpgWeSltkfJ52Cw/dK/dQ3yXtvn+1iPq/CMz5D3tZ1riPjxk08+wf7+vouwH4/HGAwGLnq01Wq5jWVib9/cIX7hBj7fxdol3IzXk4SVX3R4l0olVCoVZ4cyAlbxj48vyhv72WIeJStPrcNk03ZGaKMmhDk12kV1Hv9n5wXJzhNeo5ypVqsol8suc0PHjIWGnz596gIGvv32WzQaDVfUXXmnmxV0slGXctzpR/CNl9oRvO6LvNF3+sbarj3auspXH62sUWMXl52ENO4ZUqQdtGHwvkYCcIYDc2/1/dyxoIFBY0IFutJ8PnehrjT2OLi24KsyU5WQPlcXKRV8KGzQt0AtmNfJbkOg6P3bNA2HQ7fDSqBuFw2V2nQ6RavVcjxrNpsu3YHfZS2g+XyOUqkUCSUDbpx0dMTl83nMZjMnDLXeBQENv/vkyZPIriHwTon+9Kc/xddff+2cA3TSqIPFzlWfArQKxPedZdfpPWc9mnq9jsvLy1ve3PfJhV9GIYMh9A6fsuTfyq+QQ0o/cy7QmOPJaBcXF5HjRfmjIaYcY+Am9JT80/fw+ZlMBvv7+yiVSs5ZMBqNcHp66o4/5u6FD/BYcMC26f8t6PEJVo2A2zT5jCM7jssMqGXXtE/6N+UYFZQ6ZDSVQneVrBON32WdIgA4OjpyKVP1ej1SY+Cu8zW0RrUf+hw7t33XCYw3nfrE568Tcq191PZzk2GxWKBQKLiIUs2XtkQw2u12cXFx4YotWjDC6FELOvTEJ96rNdyWFYYOAS5f3+5KoTGnjty0YXgX8skU3/9913zyVNONWYtmf38fDx8+RKVSQblcxu7urrvOzZJisegKzM5mMyQSCVcTTceMslTTL1hDrVqtunmlp9DYNXRX3tg+6zWfHt0UsYYLI8kuLi7wy1/+EldXV3jz5g0uLy/x3Xff4erqCt1u9051ASypUaR41hZ7VjwTWidawNRiXt5nC52qs5w49vtwXlq96evXh1K1WnVOL55kBkTnktoXtA14Sir10tOnT/HVV1/h9PQUr1+/vlXfinKXdkgikUCpVEKtVsODBw+cc7RUKrn7bfoH26VzgcTPdPJYp77yzhqp+j97zafXQ/f7vq9yVPXWJvENsWAIhy1rn/17OByi2Wzi66+/BvAOZzx9+tQFA1D+5fN57O7uOuwReifHWh1savvZzXf9HseaDp29vT3s7u4in88DuMlIWCVLQvIx9Nm3BiyfQhGCH0J2nftktrWdyT+1rxlcobyx/F2mU2lL9Hq9yBpkKuhkMnFR3PF43L3LYno71/lOPTzBhyHZZhuMoJ/1Xfpdvk/tFL2u+kHtxhDd6dQnn2BZNTlDndfOxmKxiJeThrru8PE+TaGwQJ1M0LBwtlHD23whbtYg0DDUVZPLN5lXCaqQkF4nX+2uxFOkYrGYO9nIFmalUplMJmg0GhGhwpOZWMCL3kwe66tjZyc3jQU6amggKvDQYsK+vieTSbfTWCwWkc/nI+H9vqgIH/nGR8mCansvx5gRNTRQWWj6vnYptR2heRaidRWlChLfGtdoL46/phLqGtD0Gaa6MWSVP+q1VznDiLlisejmBQvzhY5QvAuFDIX7BKHL3mPH1DqS9D5f23wy0Pc3SSP8NMWJDneNsiOgpdOUkX7c/RwOhy76kM5z3zoMza119Mm6Y7Hsvk2PZ4i3dyV1aqoS950OorxiNEW9Xkc+n0e1WnV1ZqgjVc9ZR4FGVtnCinbHd13wqZ/f1wCwa8BnoPy/Ssp7TfktlUrY39/HwcEBDg8P3Q5itVpFpVJxxYKpNwmEF4ub4uzAbYcsx5e7j3TWlMvlSFFd6mYLFP9f4Tf7O5/P0ev18OrVKzSbTZyenuL8/ByNRgOtVssV5bZz+33kjf2eYlYgGi2jck9/UqmUu8+mWSve1R++g99VY+k+KGSA3Mf7fJgOWL5BRfnJA0Tm8zkKhYJ7ljXUrFyxzjbFJPa7GrmvdS7YFiBaTJinKbLIeyg9NsRjqydXfdfyxmfo+7DBJsdS52oI5+i9PlIMORgM3GbEZDJxtdh0vFKplIu67/f7wTWhY81xVdvO54zT73LdUWaXSiXkcjlXA2UVLg3hnJBz5i7Puo/1GMKRvnmo7SKvVB4SS5LH1k7iZ7s+6YTVFO/5fI5MJuNwKqPwEolERI7qcznOoTo0vr5Y8o2Xj1++66G1rHN9FWZa69QnfqZy0EZNp1NXLIvhYlwIej9/0yjgZ61/onVT6OUmeOGJRQQtfKcCTEbf8H0KVrlAdQKxf7FYzNUeWSwW7jPbxZoBPoGuzPXlzdlQSx085QkQ9dJvkpj6pBEyOl6LxbvK91R89XrdTWbuzpVKJZeTWSwWcXx8fKsgF/nKawxRZJoSjfrRaHQr/JD8tLVzOB6smh+LxVwxU4b3210p5bOSXTB6nc8IhdnbOcxjQFlE0Ob/08m4KfIBg2Wkc8z2S6PDfM+0BhP5k0ql3I4vw7vprOH3dNz7/b4r+MXiw2zT5eWlK+7F95GHwE2NJ66HQqEQSTu0bSRZp10ItNjv+pxGq4zUTZAFXSoP9DrJ593XsbZF7exzOD71eh3FYhEXFxfodDrOcKRDNpfLodfrRa5zjVcqFQdGGQG4aq77QGjotzpfbbipbxxD64FOi03vOoVA7jpKX0E9jYxms4lGo4Fut+vqg4WcNYvFAqPRCI1GA7/97W9dXZLDw0OX/ktdrRE1fC8NCd7HcVMnnfZlHVIHBPumadB3JZ0rBFi/K1oGwIDlhiTXG9cmI51yuZxL8STGKRQKTg8D79YAdRwBK8eVod2DwcClZHBtUiZwPOfzuXtfIpFw99PpzjWs9ZLeF/jbNR6aQ5vEOM1m07X37du3ER1Co1pTWIDwLrb2w/c3fyuW5HuoC5l6xshvFsRULEgZyg0rW6tKnTzWeUCDUeu4+docors6dqwOua+1WKlU0O12Xfq4LxJG5YlGLn/zzTdOR11dXaHVaqHX67m5bXGOGufz+RzNZhM7OzsulWYymbhTZvguRp8R47LwOnENDS5GWn/33Xf45ptv8Nvf/hbX19duM8+nN+y8XDU/V5FPr+p6/NDnh2hZtoFdQ8v0N8em3+/j1atXGAwGuLy8RK/Xw/7+PorFInZ3dxGLxZDP53FycoKXL1+i2+3eeq7qPz3ZlnpwPp87HMp2KZ+4XjOZjEt5qtVqbn5oXVXbB3UO+Xjhw6JqF4bwhNq3vv9vgqzu1bWvtq1iTvZTna6MgufGNteRtlsxkTpreAqvOqUZ2cR1ytNKaVf65AZwU1uUuMcGdOh3QmuCPgjew/YCuCUXLXZlW9RvoHauttX77uB/5IWqgC0DCBypEDkQvs7qxKOQ7XQ6juFXV1eumG6323U1LVgPg5NCiymyTbrAaERQUHPXQkFqqMgkn8/B037wuFQF0coXXewcAPUiWiGtIE6B9KZpOBxGiriyLowuGPJFi1jqpBqPxy7SZjabodFo4PLyErPZDKVSKWIgUInxSFCeKsOTJ+gZ151fEgvDKaVSKTx9+tQBW1WqzWbTCQCbhuAzwkPAbFkKg36f4xyLxVy6B3fxut2uc+p9COD1kc4zu/hD92ubOQ+t4atzz/7W51hgSqcm17EFQ5rWQYOZ46/5+7bN+pxUKuWeZZ1LFrhZEKIec2swhuSZDa2279gE+XhrP9t0Pm2z7ZcdZ22zjjMVXKlUwvHxMQ4ODrC3t4dKpeJCenmMoRb9VickwUGlUnHj+cMf/tCt35cvX3oNI7Zh2XqwesaODxBV5MpP33PJBz0dblPkkzO+tWP7xs+WkskkTk5O3BwvFouu1lVIj8ZiNydAAXA70clk0tXx8jnPaOhx55ljTTmtKZy+99rPfGcqlUKxWMRi8W4njO3/UBmoMuo+AOl9k8UmicS7I9APDg5cetPh4SEqlQr29vYiRaGZ5sQoQ+bU01CnY4BrjeOeTqdRLpcj6casoxaLxVAqldBoNHB1dYVms4l+v4+rqyv0er3Isdbvw+8QMOXf9zWO1BPEMr51GXr3Og5Wne/6Q4eX4lCfbgNuUtI4PlzDGnmjTmXWpFFnwGQyuVXsVuXlMrKG47rjYY3X+xg/jbb26TE7l7QdxKd0FHNTJ5FIuDnt0wMcj3K5jP39fRwfH+Pw8BC7u7sol8tIpVIuvYopo6zdSPyqvNRxV6M0tJ58eNR3zzI9E8K0vmda+2vTa5GG7DIsre1Y1WZiy2az6dKaxuMx9vb23EY96+SVy2V0Oh0XLezD8FyrHEuuIeItRrda/MSaOSzyzlP4iMGIhy3u1/np65/qBvsdn86z46+RIpsk35wLzT2fLcH+atTRZDJxukbbbHmtPKMjTeXPYrGIFJO+vr5GNptFLBZDu92OHISj/gD+8N380eh/7acPZ9KXoNfYVt98813jvT57ZtlaXDv1aZnytmFFPsBtFTYHaDgcRnbhZ7MZdnZ2XLFDLgBNg+KPGvh87mKxiIQoWgXq283RPvH/+h71mlmPsfbRZziRP6HBsHwJ8fhDSHdd+MP0J72HTiitTcHvMWeUPNc6QJlMJrJDy1QrVV56UoGOgTWwbZQAr9EZRCcRI524Ewng1i6uT/itQ8sWHflHZyGLqNJZqQJ0k+NohZXtzzKFbeeyb676BJN+lyH0ChA1vF5JlSwAF43D6AymTVmgyffw2VS6ep3P962VVQaxbeO68+H7IN8YAdGTXYDljgnLF+1jLBZzzhca6HZHxqaHKnHXHkCkmFupVEK1WkWz2UQmk3G7GrbWlk+X8O+70DprKgRuNkWbXtfcmWWdmlUnNvm+V6lU3IZEpVJBvV53YNJ+j84VLVjKz3cF11ybdDCo3Htfo87qhN/lOg0Z8eu2SftAvnPMaGAUCgX3Q1mpRo8CS3XM6PomkFQACtzUiyOYPTg4QCKRcO+9urpCo9FwGwxaV2PTtI5D5K6kco9ych2nsO/z+75f8Z3KcJu+RIcoC0erg8HWTaFDlc4aYg4tBaB4ahVPdZ1Stmjk27rr/r7WosXnIR2v96vzk3WKDg8P3QZPt9vF9fU1+v2+uJBVdgAAIABJREFUK0LLnXWtucbofdZT1FNJATgjTh10NPB9/OH8W+akWZdCxvJd6PuSn9ZeUruJc1pxBXkUinglH5nN0O12nbENwK0F3ZxgUdiQDFCcqfWfLJbWtcxNZm5kUU7zHo3OsbLA0jIcdJfx/ZD5sC755rbPVvC1KR6Pu8N4NFuBEc4a/aIBA1Zf+pzY5HEikXBOVACR9aZtUt2ptK7DWvvse76VVT794sO8vmeF6E6nPml+n75QF6AaVMpkfY4KMj3Bhd62ZDLpnDQKHDS3mwuTKTRqHFL5qCFJryh39dUbrkzSQnzpdNp59lS52UHhu7Sv6rBiqJ2+S6/zOzSCrZH0oWQNPbaXApDhf+od1jGiUBsOh8jn8xiPx2i32+j3+8hms04Jco5wzBieBsClOymAVAVL/urOB0P3yS/uZtRqNRcV9Pr1a1fcmPnA1jGlpI44kgoT3qNznBE0jELi//r9vmuHtnHT4we8U0rqCPIJA6so9X969C7nJtcKDWvrXNL1nclkUC6XHaBhGOhgMEClUsHp6Wkk0syeAlUoFCJrkaHO3W43Ihe4trlzzLWnux8+B5NPQFqlqOMcAqDfp5Fo+0FjmU4sgkmezqORi9oH63Sz7+BaI+iPx+NotVpIp9MolUpYLN7txvPYeRqQZ2dnbn2yZhABE2vUjMdj5PN5HB0doVQquR2pdaLKQm22azHkfPNdtw4vTcfcJPn4HAIx1nGk91MejUYjx7+Qk8bO4729PTx69Aiff/45fvCDHyAWi7nUmeFwiDdv3iCRSNw6IY3jA+DWugpFA+j3tR1sF3Utnf13dVSHDB6CNbb7+6CQbLV91t8hoKaRF0wpLJfLLn2UOoVRLYyiYuRNpVJBtVrF27dvUSqVXHFFyldGITP95ujoCIVCAa9fv0ar1XInixHz8MhvPuf169c4Oztz61nToVc5PJR8vOE8VuevzvlNUChs3afnLamuDBkevM9iWOrP4XDooscUv1GOJ5NJJ593d3fdz+PHj13qI09OZBq1NQpZf0prhum8spjUJ0+pwyuVCnK5HIB3DojLy0unX2yhZfubz7kPA5GlD3z8t33R+Ul+7e/v4+OPP8bf/u3fYn9/3+nN58+f4/z8HK9evcLZ2Rna7TaazaZzlB4fHwOAO2GI0Us8YZbrjAah8tpifpWDLNZOuXVXZ8v78tdiwdBnjuMmZapNgaEtyFSyfD7vCt/TAdPpdFz2Q8ixokXvq9Uqdnd3Ua1WI1G+tVoN7XYbuVzORU/o2tbIb46TFtLXuqcqM4jBKIer1SpyuRwWixvHOLMRQpEzIVzqS43x9d8+w4c9Nkk+u5i2oNq3fK91SMZiMcczRj9ls1m8ePECFxcXOD09RafTwWAwcCmKKt84pppJoym6am/TmR2LxVy0vqaEKoYA4KKqKDe4+Rzioa4bTS0mD7TflmdWNlj7TG3gkLOStJajxk4SC0TsAtXv+DqiDSKwBBDJ5+31eu6kIQo/Gho+xurfHHQuTDVOdaLp97SwoobDcYGzOJitq0Ee0GHjMyZ8oFUdJhw8tmnTgFSdI9peBTnsp80fVK+j8nU8Hrv0tEwm4xw6NK65c0iecpeCOfrkudYRWiwWkV1dXSQEumyDnj7z5s0bXF1d4e3bt2g2my7cVXO/yWM6XXTBWL5bZarOAB2/2WyGZrOJ58+fu3Q7Denb5M4klX5oXfkEje0Dwb+2i9FABJ6huco2qFHHlBlGAFiFpM5NznXOC3sMtK4/u7NmDX9toy8yiNdJusvK8bf8s4aJvuu+yAekdJ2RXzqXfWDaAkALHBlq//TpUzx58gQ//vGPcXBwgFqthqOjIydXtUA0DQ3OGwUxNC6n0yl++tOfotls4uLiAm/evMFvfvMbd6rXKlAaMgosqPWtP/scn8HF8bbXP5RsuK5+Dr3Hp684NoVCAfv7+25z4Pj4GKenp86AUsW+WNzUKel0Ojg9PcVvfvMbN17T6RTX19fO6CNoIY9orH388cc4PDx0tYd4XHin08FoNIrMe5/jgms0lUqhXC47A0dDjD+U53b8fxdkwZalENDmvbpzT0f3xx9/7E542t3dRalUcsa5nnAZj8edcXNwcOCi1wjsqHNpMPCY9nw+j/l8jmq16pyv5CWdvdPpNHJ6VL1eR7vdjtQvslgvxJ9lvxXfkEebHkvf/NR545MNobH03aPtJ1ZRhwznvn7PAvJkMom9vT13ktf+/r4bl2w266I9SFxbWqyf1zk/qBu4o+zDNJQzjKYsFAqo1WoulZ0bQKFyBSF+b3oMdRPJRubrjw9nMMp6sVjgn//5n3F4eIj9/X388Ic/dBsIu7u7znnZbrfx5MkTl3rYbDYBwEU7KT4hLuc4E+toO6zhpc4sYlffOrBkZZ3+XkevhMbPGo6KoTaJb6h/iF1IKgP/4A/+wOGZdrsN4HZkl7aZc4G2yosXLzCdTlGpVFwRYUZS9ft9nJ6euqPZdbOHbaCepPOAuBNABP9yrLhRRecQU+K4oUwHN3WmNcx9Y+HD8D7ni7W7ra5R3twHaft8AQraVv0OdQ5lSjabxfHxMeLxOKrVKgqFAi4vL9Fut3F1deW+Y3nP6DbaCJwD7C/n0HQ6dU5V/b46tdXOtPJZ+xeyfQB/qRIrA/jZvkPvsZhWI2VDtHZEjW+ALFGBKaPYQX2WdZbwGhUVB46AjwpRC7ZapliG2km9CiCwvT4jUZ0Y6wDQVYDAd/8qoP8hxParowaIHjOr6WLLjF9OLPUmM9UJgIvOIIC0xZ1s6KhP4amxqs9dLBbOYcO8/SdPnjglwLayxpG2V3nhU/qrlKJPgXIntN1ue0M4NzmWGv5sBaN+9v1PhYbWG9HwTTWuQmDCzmUqOYZT+3ZKFXhZ8GifbeeFrlufEl9muNv2WrCyir4P49DKCfZTFQ7BQ2i3Rj/75i6NBJ5WwFNlSqWS262lg4a7E+Q/j1XXtcI5RICsoDqkEO/CD1VgPmPRNy6rdNJ9GIergPNddAAjGtlWGyVgn8F50ul0cHFxgVQq5Xb6AEScLSpj1VHKWkSMrOx2u644ujoDQ32knGUqHYESHTV34fe6gPZ3Qcuwz7I5wB+mu9BJQyOdJxjS0U1jQTGI6ineQyNQdbDqbXvapc9Joo4GTYfkDqNGtK2z1kKANMSv72stWrKOm7uSBeDWIFC5p46cePxdfbD9/X3s7e1Fjn/myXla+FZ3fTVqQ/UA32kPMLBrhu2jM14dDcQSOtfWkVv3sR59J9Ut+1Gaz+euqOhvf/tbF+19cnICAG73nDW4FosFjo+Psbe3h4ODAwA3afOKael4UKxr03ptiiIdnXqIhhqPPl5a2e7Dmj7SdRYynEPvuQ+yeIS/1RiNxWKOZ7qRG3oef7hZrpHzKsuIY1i7xkY7AVHcqdhfy2RYUscOo6SAm41Y3UQL2Q135fsy28Ty977Ihy3tZ9/91EEsIAzcrD+VeaxFy009G00Xi8Xc6a/MsFH9xjnV6/UA3JxIyvmgmEfLKdg+3GV8dFw4l3UTWO+7C/5dxz+x0lGjD2CH1eCaz+eRSvt6Aoye4KJGs128/EzFwerpg8EA8/ncFVRTI0JzDK0i4wL2ebLJbGso8ruqaAFE0kN0EJQ//J7ufNBw8Q2YCm41RFeFnb8vaUQNHV/kM4mh8Nofji+NZh27Xq/nxm8wGDjHDKNZ6HWmt7NYLKJcLmOxWEROfLJCQMG+Ono6nY57H43KbDaLTz75BNVqFScnJ65o4uXlJc7PzxGPx13oN59v+6c/qsx846AAdj6fu5PBOp2Oc4Rx3nDXa1NULpfd/PJFVZCsUNf+MHSXoJ9hgtyZ074tE8zaBqbUaA0iPsMW69K/+X+VAbFYzAFKlR2UJdbg8PWftI6QVAFrQ+Xvay0qaT90TrIdDIe3cskqUXWsap845ozUIECic5H9ZJ2EnZ0dFItFt5NL8Mtxodykk6DRaOAXv/gFGo0Grq+v8eWXX6LVakUcbVaJ2f77+KHrTHmiY6R9XGYwa1ripigEyPR/Vt77nrFYLFxB8i+//BJ7e3vodDp4/vy5S29R+cvvce28ffsW19fXeP36NWazmdut6nQ6bqdJQQvDbCl/Z7MZhsMhrq6u8OrVKzx//twdh2nbauUjjXym8cRiMVxdXbndxbsASd/z9X/3GdUWak/osx1nHylGIX+Oj49xcnKC/f19PHz4EPl83kUz8LQba1zn83nMZjNnMA6HQxf1wqN/WWhzNntX1H8wGCCfz6PVaqHT6bjxpHOV9f5isZir80bdpWnh1Pc+smPkk7U+fHMf5HuuxVzL2qbjuc7YKnZTOae1ZJhSQQcY0wA+/fRTl3rG3fzFYoFqterW+Xg8dmkimrapTgDKSMp1dbpp2/SzOmh46AEj2NWICa3DZfzeBBFH0Onrk/96jTwAbgzt6XSK//u//8Pl5SUuLy9d8W1iU9arIW5jLRs6qnu9Hvr9vsPCxKCUxcViEcC7NC0twaCYtdVqoV6v4+zsDJeXl2g2m7cK1Fr8Yo28dZwzof/pnPfpSYvnNqkXaa/Z6ASu//F4jGfPnrmNVt2M430+pwT/pvGvRWY1WoYp2ul0OhKhpjpTMwJIPG1RN5yBaC0+bmbF43F3+A3njNqHylddf74+ad90TlNPc2xsgIB1MtwXTiXfFKNa+5btU5ueuqper+PBgweujdxk39vbQywWc6m/1GFayB4A9vf3sbu7i3w+DwBot9vOhuF76vU6hsOhk6eUvaPRyMk7zjWNIrPzR1PErd7Q5+gmZUivWR8Cr1n5ZX+WjePaxYT1mv5omP4yT5bP+NNnWEGmu7WsV8NBAm4WkR65pjtRXNjqfVNDUSeX9o/ftV5WCglbfE2VuwKbkAFt/1Z+3RegYTutI8vmHOrC1PbpODGkjd7M3d1dPHz4EPv7+05x0fPMkOB0Oo1iseh27exR1pb/HAOm5fBoN50nfEYymXQ7lNlsFicnJ6jX6/j666/x6tUr1Ot1XF1dufmkwlj5vYz3PgFJYp45nRa647nJkMRareYcmErq4FOlwP9RINHgB26cSNYo16PR2V+r7JU0TcbWjyEQ4tqngUDS3UAKQTrgbF9ozNgTtXyggz/ki5IPnOv8177exxha8s19Evtoj821c9TywRp7rEPz6NEjPHnyBA8ePMDjx49xcnKCYrHodvU5fnZMSBbIc7395V/+pXP8VCoVvHz5Eqenp/jqq69cZIcW2rOy0wdebESJHSO9xs8hA99G9G2SfO/1tSN0jTvt5XIZjx8/dkWZ9/b2nNPDPsM65OhsoYEOwIXoW93IEzKOj4/x5MkT1Go1pNNp53Snk0X1gk8269ocDoe4uLhwaaA0/NehZbwLGRzfNy2Tf7ZN+n/uIvIo2UePHuH4+Ngdx03Apz+UjwpsmfLKFGMeGUwjPZVKOTlFXanO7nK57HafGQEKwG3YMB1mb28P7XYbi8W7TRGt9xaaC/azD2haHX9fGGddCoHhZeOsc14jPtUhR2xp8RKdZAAc9pxMJq64M4DIpoRiVN8mozot6LwBbjaR1CGj0QVMgWRNiMFggFardSsii0SZY99tjetNEfGonsiqTmrrfGIb+Zv389CLeDyO7777zsnEXq+Hi4sLtzP/4sWLyImwuhmixh1xLB2ndNBwjema1Ro2R0dHDqscHx9HHATL5v9d+Rpaez57w/Ju0+Pocxyo/TQYDHB+fo5yuezSOLPZLEajkcPQPltQn8N0GdbPA+CiEln/i4caaFQpnQTccNJNTz20xtZboZOT76D8pf6js3tZTT7bnxCG4b0he9sGSOja3ST57HMfztT26PfoBLu4uMC3336L4XDoorNns5kbi9ls5k7qUvnLvtF5xhQo+gB4D+3R+XzuUsSto8tGa7Evas/raV8Wm5LXdk5YXwY/W36swqvW7g7R2lv+1qixg6eTkYxn+CYAp8z0xzdxFYCOx2NXsFWLxGrILnfzNZff54jxKVqriKyx6RNqPgEXYnDo/hAtAzwfQj4A4uuPddKo50//x7HmKR/FYtEZbhx7NdKBaAqMndjLeECwoxEZfA7HmO8slUoOgLJIGQDnreWitM4InbuWT5ZfPmOfz7MhxJtUggQJvnZZCs1f8o1GRLlcdsKPuwoaUm/7rvyh8tOTY+zaUSGnYIuC1Dp21WFjC7tZQWbb4yOfkey71weEvy/i+yzwZ9vz+bxzommRbP3te546algfQ52l/E0DUKMu9Pv6DsunRCKB/f19TCYTlMtl/OAHP4jMK9a9aLfbkaLVoboxPsPX19e7rKtNGxR3JZ+iVkO8Uqng8PAQDx48cOlL+/v7ePHihathoQ5HXXtcKzzu+fDwELVazc2XZrPpojHm87kzyE9OTnB4eOjCirW+jC/tSvuh7V8sFpGi/5Qfd9F1y/5ngf/veizXIa4LphuyKCx397k7S7K4yhKxj+7KWnnOVI35fO4c+TYlRlNmfdGMPBmKtRs4pu/Le998IR6wDvTvi6zutnNrmRMnNCdJIf7YvmsKCMdGdahe02Kn6rjRd1oHvm7cMGqWz1FnjD2qVjHQMh1pP2+KFNeFjJoQKY/1SHOezqOpSNPp1NkOrG+SzWbds5gixcgyniCkMpcbkVqGgXOL+nZ3d9cd0X1wcODwldaL2hStazeE5v2maJ1+LRYLd+rZycmJ4+tgMHB6K/Qc6pterxc5zETtQnVSq5OP60F/SMvWvV2LGjEP4JY962tzSHcqvtH/+drkw773qQ9D6922WdvDz7QhGHWdSqXQaDScfaj2O6OryFflCz/v7Oy4umuTycRFgysens1mLj2Nth5wc9CRrYW6yjHiGx91IlpehGRWCLdanqqt7KO1Up/4m591l5KGXywWc5E1NLiKxSIymUzkJAgayyy+SgBvDdzBYIB2u43Ly0v0ej3nxY7FYi5Co1KpoNfrodVquarhBCsELlxsNE51Z4p1AYCbAVWBrIZ3SKEr85XZdlDUmNewK/VKqhNpk2SBhW8S+eq48LPewx2I8XjsBGK/348UW2OEBSNYtP4Nw/HJC/KB7VGDXncLaeSpF5TGvNZcodJ9+PAhFouFC6VtNBouKsOCXetY4bPU2cDrKvh5jTspVOIUHJsUpJVKBY1GYyVIVANbhZIKqmKxiP39fTx69Mjl/XJHFoievKVzgs5QVWAKeiwQUaePglAWaLNHtqvwJh85N/S+dRwqum5DjhgVtNYYDgHyTZAP9GuR5Ww26+TQwcEBzs7OXBqDrUWhcxhARLYwgqJaraJWq6FQKDjArnJd5YFVUHZdWJ7o2vvss89wfHyMVquFarWK09NTXFxc4Ouvv0az2XTpGurc5Du0Lav4vgxY6Wc6vfQdm6R154dVwFwHmUwGn332GX784x/jJz/5CWq1Gur1Ol69eoWvv/4a/X4f/X4/crIh1wkdt5VKBcfHx/jLv/xLHB0duRPYisUixuMxzs/PHS8ePnyIL774An/6p3+Kjz/+GNlsFovFAmdnZ0ilUrcc9cv6zDHUFK11IyVWGX26PqgPf1dOmlW8sM5i4pN8Po9qtYq9vT13egWLr6fT6YhOVP0Wj8fd35lMxoFRnozIvP7hcOjSwzudDrrdLnZ2dtDr9ZBKpbC7u3vL+Upnmu5Y0oBcLBYR55KexBFyrPp44TNKVB5ZvXJf5BuXZVhOf/vWK++3kZfEtIoRbIQrcZ11jtFg0bkxn89dCjfHgnqy3W5HdLo6ZzhnuH75bDoeWB+iWq2i3+/j8vISjUYjMrY+nWD5EDIkP5S4IWufrWNmjSbfJg/rAJ2cnOCP//iPUa/X3QETLEy6v7+PP/qjP8LDhw+xt7eHVCrl0kUrlYqrJ5VIJNx6sTaEnvClY075DMDp8uvra4dZu90ugBuMRQoZcfbasnnt453FN4rf1nGC3YVCkbAWj3zyySf49NNP8cUXX+D58+d49eoVAOD8/BzdbtdFSZD0eUz7vLq6wsHBAfL5fCQ9XCOeuOZIxFh0zvG5Gs1t165uHGqEo60X5otq8dkKdlysTNUNUD47tLl9XxhVsTD7aMfV2gYqk9QhfX5+jsXiXXonDy9g9BM3EHK5XES26ZylrZ9KpVAqlVAsFl0K8HA4dFH9TMOmA1WjqTT7Qo9vJ+7mKasaCbxKb+h9nGdWPum4qN2vz6YdtQp3rUx9UiHJF9iG6c6LevMVfBBs5nI55/FKpVIuJ5SLUN/NAVdHDY1DnobAVBpOKDoIOHmoKJUpWriP/eNz+Q4F+DRS1ThS8il+nxC2E1wnpOYjbxqUqvGmRpIF5nayWEGuhd6Ad2GI8XgcFxcXePv2rdutmM/nkbolqVQKxWIxcjy6Fr5TwKHRM7HYTb2ier3uckKn06kLh6NwZh6yzoV4/F2V8Uql4k41efHihQvRZwE/dSj4eBECdABcyoC2n9/ZJCBl1APHzBrmKhgsmKPDjOPebrdd6DV30w8ODtDr9dwOlI2g8u0cMPyeDgYCUgAu5YZHjGr7udb0+FKG8Wu6DftHL7qvaJsFLvY6yQI9ex+dSbymCue+iW2jAtEd8FKp5OYqx8VGM2o/NGKpUCjg+PjYpT2xgKkWD7a1hazit3yz96XTademw8NDFzEwGAywt7eHRqOBYrGI7777DldXV7i8vHQpUTqW2h+VodZxFDLyfeAeuH2i2ybIN4/s/LNKWr/LnT+e+EMjPJlMIp/P48mTJygUCm4zQQ0CLU5LZ42CKQAolUquWDRPyqPxTtDDdvGZsVjsFihkP7QvVq75vrOMfPMrxFO953flqFkFgu04MzKFznCuN41s0DEDovqZp4gwRYWbFTQy6Lwh8OTam06n6Ha7LnKn2+2iUCggnU47WaJrjD96alEsFnNF+Xd2dtBsNtFsNp3uUOeAxQa6TkNrwToYNk0WBK9yKoTmoa/9auDqhprWeBsMBojH39XFY4QAI1ZtyoumC6sjjThYx1gjazRlgxuepNls5mQ6nYUsIM80RzrzXr58iTdv3rh0j3X4qrbAptcjU2OYhhmSQZSlOgZcI9xE5DH3//3f/+2iOi8vL53NMZvN8ObNG2cPUBexHYo/geiRviTywG6W6CY0a3XxmOJut+tOudGIcEurZKHlSwj7+AxHGq82EmsTRPvCriG+Yzp9dxz8r3/9a1xcXLiU+3Q6jf39fedw0TR9ywumrDSbTSfjNJ2mVquhUqk4Y576lga5nmhJHnJN2fQlTXvSTWi2jak7NmXKpy8VAyjO1PVk1xe/a7Ge0n2sRTt/dJ742qJyX9s/nU7RbreRSLw7VZA4hzYDbbZSqYR2u+1ObiP/+v0+ut0uut1uxNmiMpNyMh6Pu3pUsVjMbT5Tp+nptGybtfd8PLB/6+aH2lnL+Mh1Z5+jvF2FodY+9UkHYpmXUIHDeDx2DdI8Qv5/MBig0Wig1+tFDCM+h6lPFKQUNlxoGiFD4wa4CRe2XmuCIatsyTi9rkBfPaY+pb8KnPgcIKFnrQKG70NqjFpH0TJhEBpnjjWLarVaLbRaLWSzWedUowGg3+GPDeG1xLHQ9CnuLrPAKfuh+cU0avhsHn3LcR6NRk5oUDAQTGktDV/4v08wsR3KR+tJ3RRp7nkoqsRnUPM6+0Q+0UnFZ1F40oAfDAaRNc1URPIIuJ2uw7VjlaNGWOnOH9vJOaHRGXY3hPeG5o4+z7ceQ9fs9/Q99zWWts18DxULAYP18uuumAJJXiNx7bC4Hg12KgzuSukYsT3Kf1W6uhvAsaTC4/q5urpyazQWi7m1R/mux2FSAWtusOWLzxFl+8q/QwB2lbG2KfLNLx9xnvPIdBrywI2jl1FpbLcaadxtr9VqTveVSqUIGJlOp+4+Pofzgc5ylZMa1REaC9tXdYDdB1Bcx4Hz+0QqxzTKUIugq3Gk/VL5qUXph8NhJFVG63oBiMhk1Vlc4+Px2MkT3s+INs61xWLhIlHptKGxyhon3W73lo4Ews4afiZfQmB4U3xfdi20/q2eWNY2a7D43snNHgAu6tcWzQeipzXxx/6t692HSbVdqp/ZL8p1m5LMunSJxE2Ra+KpTUcA35U4t0Inxvnwp5X7aoAlEglcXFzg+vranWjHdcfNNY4RZR/XE9ec2hF0lg6HQyfDKUPpNEulUhiPx67mCp2tsVjMpWIxrdAWGL4r+XTeuqRY/z7XpLUNF4uFc7LEYjGcnp66TYLd3V2HE1j3hRHcVh/poSUcNwBu3nPu7+zsRGqXaNSTpngD0XIevK6OWMVDtG19Nd20nSGbUJ9FvMy5oOPqs82+TzzD3+vIRvsd/qbTstvtRtYWZZra8YxWImmqPDES03P1oBo9uZBBGAwYoGNIN4HZJ44vbatlfbT2K38UF/vsiWX2yF304kpHjVXKOlEI2G1DKQi54xOPx1Eul1GpVHB0dOSY3uv18Pr1a+dlJnCg0uv3+7i+vo7kLnIgWI2bR81eXl66xcbq7QxbVEbp4KgCtDnBNBQJeGgwaSi2ncQ2RDsE3u09ugjVebQpsgteATr/T8PPKkT7mW1TZ9v19TUajYbbxWEEy2KxcCGIGqIWMqq5qNhezbHmmDJqhouzXq+7RUsQGo/HXbGqZDKJk5MTF33z4MEDp8C//fZbNJtNFwLO59sjakNCWIUrd3QYFaFRWZsgLV6nO+x6coMVFBQkBHIEFd1uF7lcDq1WK7KbSp5SeercGI1G7sQRBes6Z7meKIgtYKQCpvNVjQuuSzVadKeZEXVaDT9E6zhy9LNPoXKX5T53f/X5CrrZbxrdAFy/eY9G16jxsFgs3DgWCgUX1ZROp13eML/LmiIMvZxMJpGcbO4eaWRkPB53xhx3kev1Oq6vr/Hs2bPIEbOFQgHlchmPHj1CsVhEu93GwcEBnj9/jouLC7x588atNQt2Viky64jRawrIdAds06TziH/bz3aOxWKOtDyHAAAgAElEQVQ3qTEHBwfY29tDuVx2juRGo4Fnz56h1Wo5o50RUDs7Oy5C8OjoyP2PBWqr1arL5aYcZloLozyoh7lDzLVN0Kl6wvZT+bwJ4ztkJNsxVVnzfdO6DjhSLBaLHHlPfUmQyvRdxUq6oaHrQB3burFEOa6RNxpNSj3U7/dd4Vg6XzqdjmsrZWwikXAh6fP53M2RRqOB8/NznJ6eutNseMKh3fBh+y3PdCff6qhNkJXny8YqZOzb/yvAtgYLx0z1A7Erx4V81yLqxAeMpmOaE3Ey05wAuFpGqle1DXajRiOmqLtYJmA+n7vDGBKJhNMFLDA/Ho9d1NQyvGPx7qbXItP49DQeK8+tTGXbqD8YUcP/EefRKUPdxY1dvoNrQ6OfuO5YKJ9ZAPF4PBLlyI2Ifr+ParXqIqkajYbbEOTaLJVKqFQqtwqYKs+XyUL9W/VfCMfY5+kY3pdO1HdavcexYuTZ8+fPXbruycmJk4Xj8Rj1et1hEn0eHWg0/Dl2Wk6B9p2eXAnc2LJavwa42aiwekajcLiG1fHAOpi+KG8dE12vxMT6ty+6SrFgaDPkPu1F/l6GA9gGn5wEbqLhGd3E8eI6IsbN5XIuOpi8pC4kX2j/pNNp1Go1xztuPhWLRVSrVYxGIySTSZydnTnfA++hLUpiBDMdPBppZvuoDj5iZF4nRtb1p3NA+aT/V3t8lTxdK/WJLyLz2TBfuJca/IvFu5N7CAiZWnF0dOTCt1lrhPULrMdtsVjcCoGiIUxDhOHj3IFSD5oqX93N0EluHTUK9HUh2Bxjy3gbAaDeUp/CXywWkUrx9xGOyDHTdvraon/7wIv9zHYSMLI+CWsq8DnkNdPVqCQ1RY2gxobh2ufQ40qgw+NLtaAlxw2AU4i9Xg8HBwcOQD99+hQnJyfIZrMuqoYKudVq4ezszO3A2DnuW1TKPwKGTafMcMeJ7dHIMx0f8lSdidqWxWLh+tvtdt2uL9fjfD53x+DpbgWdr+12O7J7r+OqSo07/aw3QyOAXnAr9DSiB4g6iGlgME0yFAGmYxEiq0h4jfNGgYzuVN4XqaJTxxX73e123bhbp47tF9cJjffHjx/j448/xvHxMY6Pj913CORVRqr8sdGGOzs7t9IlqSi1xgLnDI0DpqWmUikcHBxgf38fT548weeff46Liwv813/9F169eoV2u+3qivmi2Zbxzn62MpmKf5MyNdQ2C6L5WVNGNSWhVCqh2+3i1atXaDabaDQaePv2Lf7lX/4FFxcXrp4WNxwYtt/r9XB6eup2a1ls76OPPsJHH32ETCaDBw8eoFgs4vnz55jNZshms/jkk09wcnLiABHlFPVyPp+/ZQi9Dx9CZLGEXvcBXf287pzYNN3VIKXhx/Vi00NpTAI3kW8sxJ/L5TAajdBqtXB5eelq7zGMm89XHGOxCR3v8XjcOW7USUInDtconxWLxZxcjsfjLi3v+PgYH3/8MRqNBl69eoVnz56h3W47J+8q0ijAkMz+EPI5a+3/1sEzy3AOyW52MQKJTjk6v3lqlkat0pjnJoXKTz0mms43bhxR9ioOoXGq0SPAu/nE9tDxRp2mp10y5YD1HxhpvMwgVFxFXmySaJSrs9iOgRq8drOUcp74IBaLodFoRP4mzgDgdvK5maCbfzyViBsX3PwFEEnLAd6lZxCbXF1duU2NVqvlorXp/Ewmkw4fZ7NZnJ2dOd2ukXO+vi+TQ1Ze+sYwdM99bmJYJw1/kyfPnz9Hp9PB7u4uPvvsM2QyGezu7kZODlQ8oO1nOnyr1UK/33f1UGu1Gvb29jAej9FqtZzjjxv8s9nM1fqzTlCNeovFYm5zo1KpoFQqRbASHRB0gi/DodYm9tU/JdZUfKWOmmV8vo8xDDlffDKc61GDNngv7YaLiwvk83n0+308fvw4Ek3z/zH3ps2NHUf2dwIguGLfSHZL3W1pJNkz9niWmHf+0vNJHGOP429Zdq9sEsS+cCcBPC8Yv8S51XVBthrUMxXB4AZc3FtVuZ08mVWpVGwymfjJTqwDTLjRaJRoDK7Pi53Dvprd68dqtep7SPv96MEyWn6fFgPH9D9DE5k6b/p65kcrGkKfRgkTaePRx3Prz2mBUngTGlReXl56fex8PvdmwzgPNAXWDDGZ4+l0asVi0S4vL61cLjutDSVbq9XcyYTuG24ipcVpB2+t/Q+PNOXe1Dg85Lg95CiEc6bzyOetGyFNC2jSnBMd4aZiXrSmN8wQaP00DkXY4wcFQ9YCwTFbHt0M64mAUgMeDfTpf0K2kGtzzfl87mUWGoRSGqVlAVtbW54Ri6HZYQDGUHBPM5vrGtxTWD/NvTAfWg5Iliqkq+vA2ZzNZlYoFOz29tYbGGLkVMnBirm7u3OWCxlA6IvqPOr9KSU1lKsYxZR9psfEhko1HKscm1UOTOjEPEVQoSN0yBQUpu8LmW4z87IIMn3cL/Koc4kuY31w3rVZY5iVDf8Wgg4YYt3n6FRYHJxuwV41W9aUo1vpm7G9vW3D4dCy2awNh0Pr9/vO9tGyAZU/vbcYYBrTvepArHPt0v6Wpv/Z4xwhWqvVrNlsevPz0Whk79+/t+PjY2cJsq56uhNNR+nvhlxTfjqdTh2EKxaL1mg0LJvNevkb/2MPaRAYzle4979kDtNAGv1/6NzH9uL/9YHtCcul9d7R39g/1lAZvgQDmUzmE9aZOngasKvPorpWE0CsOe8zWzY6BEgCwGGvkbHM5/M2nU4tn89br9dLZWCl6ZCnYLeFvmn4Pe1e9P36PTbSbABNeiuViutXGGwafCv7kMAc/0FLENV3wnch+5yWHNQvrsm6c12Saax5Npv1YDX0bUO/MG2+1y2LWu6F3xT633w+c6VzEDLRzCwhg9hW5uD8/DxRPsN7NEGj5aAKpOg9aHmUrj2JNWQM0AwgAp8UFtHnACdpezj8Oe19rPe61/Eh/0yfEXav9k1C51QqFRuNRu47mCVL8/ALAT+vr6+tVColepWQTFSdo8lglZfwiznCjya5rKD4qp6laYPP03YA6uNqXKTzyc8P6bB1Da6vPsGq/ZmGA/B61mo8HtvW1padn597jEGSmDXTw0gAbtFd6DQFuPWLigMz8164i8Wy8bP6O6oXNLZ8aD7RI2pblY2jcxIbMZl7zOc+6tQnNk9IwdTgPVT4YeCD8Z9Op05Hajab3hR4Y2PD+v2+CzGbl0ayCPBisXCBajQa7vC0221vMKUCxUKEmS2+Y7hY7LDXBgGJdgSPLcqqACeNUmWWzNKok7XOEQtquG/+TxDGPelreD6MFSAI14yxPVCKZInOzs6cjgsyjUEbDAZOvSeYoTEUJW6ANWF/EwRma2vL2u12Yi4JGDOZjHW7XacY7+zs+D0CEBAcF4tFP0VjFaMmlt0hcNXjAdc1aKYMCqyOFYqDjEKlUrFqtWqdTseGw+EnvQV4ZgAqsrBc6+LiwtrttjuKGBNOYlMmHLJSrVb92ESCdBwdlKWZJQDRMOAHqGEP4mASEDAea5h03UIFqQAkyj80kE8B0qi+VNlS4LhcLrthOjg4SGTnoHGzLnwpAKknZyEf2ofGLJkdwGFg/sMGwwoihTLAfieQ6/f7NhqNrNPpeNZ+NBp5b5RCoeBNbWezmVUqFev3+/bx40c7OTnxLIqWa2iWKZQ/HWFgpmyBpwryHwoCuU8ciUqlYs+fP7fDw0P75ptvHBBtt9vWbrft5OTEHX72A+wbetrg2ODE7uzsuK2cTCZWq9VcNmk63Gg0bDweuw7UMmPVcTgfIbgV2rd1Btyr1jH0J37pkRbMxBxTBU+0t4XqGmwebCaAcWwY7FAN7HO5nJ2dnTloogG9lq1Rsqp2SGUZG0+Wk2uYmWcjs9n7ElUF4SnTaTabdnt7a+/evbPb2/vj3/HV0vaKyqE2fV3XCIOKtPtQfaG+afizBkrh30NgvVqtWqvVsq+//toKhYIHjQo0YDs5VhiWDddQn9osWS5wdXXl/aQULEibPw08dc0BisI9io7lXnQuw+vG5nydgx4Te3t7ifI6tTuaoVa/mWcI7zsM7lnb6+tra7fbtrOzY4eHh97cHnklmKRkjIQXgb/2umCuWSN+5r4UqNne3rbDw0OPpa6urvw0RALT0PdIm+fQ5qzSU6GN0kT0U40YwMDvfHFCHY3LK5WKbW9vW7PZ9NOxkBsF47QUn2O6sbGZzP0BDOfn51Yulz3pATBG1QWywvvCwy0AfAqFwidMU00+r2Kh6RrioxPPwB5Xnw69sArwSosv1zn0enoPJA7QRaovQ0xA308yHv+jWq1atVp13QYGALOG6xM7Yqu0STr7ivcWi0VPau7s7Fi32/W11ibS2veGJD9lV8SKafOtsQoxHnvyMWXjKovh3nhoHR996hOCHW4eBS9Cg8gi8nV3d2cnJydWKpXs+vraM/A7Ozu2v79vrVbLgzOAAMqmYNbQybtQKHhNr5nZ8fGxAyJahqGTy6KQBWHjseB6slTIqNHnCJWP/qyGRYNRM0s4xbHg3+zTs9rXMXSt+D00YHy2vj78uzrv3DObXXubqOMGKANIw3s0ICdIa7fbtru7a41Gw/b39+37779PgHZ8aXYakI9BbyJqXOmho/WRBDJXV1euMAA6isWiB8cAUmHpkK63Ki11ptbNqMGga3ZU7wkwhLKw/f19p2aqPJqZH+XKMb4Ap8jT5eWllUolV5S6/uPx2A2hmXnWtdlsujLOZrMOiGmTRNbezFz5MlfqYIYZLW0q/lAGIxY4x/Y6zxML+HVe1y2Lisar8wkrTIHd6+tr6/f7zjjTuvq04GFnZ8dqtZq1Wi179uyZNZtNd4CQEwVzYECF4KvKFEaa9drb20vMG45Tt9u1Tqdjg8HA3r9/b2bmZWu1Ws31NgHn4eGh7e3t2eXlpX333XcOtrfbbfvpp59sMpnYdDpNnGyi9xSuWwjMqTw+RSaf77Ev/kcSAIfi1atX1mq1rFarmZlZt9v1kzCOjo6cVaS2h6MnX7x44f22kHN6S8CUoeQQQOf58+e+5y4uLhxc0zU/ODiw3/zmN+4wDwYDP12P59D9EAJoD81R+HPa31YFGk8ZVKwaDwVK+jvzDCinmV16r2lvKdaCfkK1Ws2z/Dh/6Dx8InSi0uRj96RlpfP5smxOmRZk8wk4SJRcXV1ZuVz2/7G/arWa/cu//ItVq1XL5/NeBgVLOm1eQn21zrUMwZc0oMYsziLmvjRwTXu9vkdLz0LmhbI0QoAg1gAVva69UngtTWrDMisSG7e3twkb3Gq13Dfm2TRphj9AlttsmbhB3mO6MjaX6xzlctlGo1Fif/I56nfG9k/o14b+LbJJeffLly/t1atXdnh4aMVi0czM5xwmImW73A/6mzJvQFZkmLkjfqBsDVYyTNL9/X1rNps2Ho/t8PDQut2uDQYDe/PmjZfhpwGF4byEI/TbwzlTuxjzI9c1Qp0UBv4KGE4mE2dzz2YzBz/N7vdELpezk5MTB850Hs7OzpxFyulRpVLJ9vf33ffv9XoJW6afD3jM2pbLZev3+2Z2D6w9f/7cDg4OrNFoWKFQcBBVgfKwdEZHLC7kNE5KJdWHUn8lNpex665bn8Y+i89A1/E8MTBRcQLdj3d3dzYej83MvJE5YBjlT81m06bTqdsgAC1wAvxl7aMIaIL/Y3avU2u1mscMypYJ/X1lnNOjNK2klxgLP4sEBixyZerw+pgtCcHTWGIsHI8qfVJBDwdOnFLpwiCChVosFnZ2dmb9ft+VVrVadUo8zQ/pn8HEhOwEhJ+AYbFYJFDKMJDW97HI1B9y/VqtZtVq1Q0hAJGCTGnOiN4TX4BDil4rYBMDS3iNOsXrGAq4fK5QrzII+rs6JJqlUacyDXHUE51YSxxUZVahkPn94uLCdnZ2fH4JaBXYwTmh8RfXod8Nwq9Mj729PXdUEUCduzTGkSoSDMG6Rli7HQo+847DgaMXO9KZbFq/37fJZOLOBfKmwBvPwnMjn6DVrDPADI1myeYy0AkaaJgtHVUUJmvJ8yjlOMaOiDkwDzmToVOPzCkgsArAWdcI9xTPSXbPbHmqGfokNORpekfpnOqcxfQhn4NTqcco4px0Oh0P2ggCACGgfXc6HQdWlEXIKRjsL7P74AB5R/9Xq1UzuwdbKcO7uLiIZkxjgdkvOUIAQXUA96QNDrXkFodnOp1ap9Ox09NTdyi1qafZ0r7CKC2Xyy5nvJ6gH72I0wqDJ5QZ7pe9BCOq2Wzat99+68dlIuPqpNHIcdW8rwJgvmTd/v9a68eMMIDXLJ7afTPzRIYydxnocJqScm10LiUx6GGYdjDwcBZpYEpQrlR9vWcGto7+JdjVTOa+nFz7mAHuEdRgZ3SofMQC6HXOexgwPBZci11Df08DDtU+xRoGxzLMCtRoSZwmKMKAVv0O9pT2BNve3rb5fO7ANyA4wGAms6T6615Dd6vdpxeaPuMvOTRgjf2d+0p7Hz/Hgkdkk2Qt4Au2SRkSZuYsB+wajBca8udyOfex0LcwqbRcX30XSpGZe3wdmARaKpXGEFrn+CWC+9Aehp/NHr+5ubHJZGLVatVliMNAarWaTafTRPKOAaPw/Pw8wV7M5/NeYry9ve1+C/pPyxLRcdorEX8UcE3LzrWnkMZxq3QLfpnKML5cLHkezhPX0e/8HJvfLx0xnzr8zFXPHF6L73oimgLRyCYJJrPlCV7op7BtBfEbvlCYDOG1ADkhcUOZjDC90YFhbM4zaxKGz2VfEj+s0kE6f2nzmjZWIgLq1Oum1hthksNABwWFkUIoZ7OZdTody2azXvoEHazZbLowmC3PSFdqGpTuXC7nR5vChBiNRh4oxrKpTDqoJkAC6Gmj0fBaRzNLBP0K0sQ2MX9Xmi/BEg6U9gVIA00wJuseIXL+cxzmEFjiusp+QJECVKhS08Cb9y4WC6fgqqPJdWFUaKYX6j/CDV3x48eP/pkYUFgl4/HYyyo6nY4rYxxZRWQpFQAtBfRhLysSGgI1CPTnzu1DI0TvwyARR5DSv3K57I3WQtSfALjdbtvBwYHLE8+vfW4wKHwGTb25LmwiPYWN4A9lylwQ9CsQQQkW2WcNavS0NZX9h0YYJOjfYgCMOlYacK2bFRXeIwyWTCaTcDTQWcgD+0lL1HgWXo+e0e+LxTKbCjNKM4GAnnzHIS2VSq7H+Pvf//53XzOAGkprKG18+/atB6Y0qcQZRib50n2FTqYkByeLhoFhlkNlTY1ezNipI/QUaxgLEmPOFAkB5gNA7t27d346iTYm1OsQJNdqNW/QvbOzkzgNkXKxbrdrzWbT700biipDhkCTE0z29vas2Wzaf/zHf3gj29Fo5AHLYrFwcFz/Fo6HQJrHBMtpjv3/ZaDGbAmqab8zwNIYY42AUfu50UgWNhk6iON+AXCm06kNBgMvi0SmVd65NuCPHkmMzlBbdn5+7utA6bGZJfqqkFl+8eKFjcdjy2Qyn5wEFYLlqtOeYs75HP09/N9jwJvw9Wo7dODLIgdhv5/wtehdgFX8QBIEmiFWprH6SgQcBBWFQsFLoLGdpVIpAdbg29zc3CRKOth32JNcLpcAiflsfYZwHtZtF/H1YvovDTDTn1cBNnxpQ28CfHQcCT091vf6+toPmeCagDUkk7vdru8BwHH0gLKuCPCIC2DRHR4e2mAwcLACOQ334GNHWhD9mP3/pSP2mWkAg9lSN8xmMw/e0V8AWVRS4DsyN2bmvoueyoU9293dtXq97qxTZIk9psAcuo24UNlrMBJhDSJP6o/rc4XrxV5AfrHdgD/4XKo7064X8zGeAqjRtXkMOSFsRxH7mfUjFqNclyqBfD5v5XLZ6vW6M1a4PoxtYjbKdOlziz6E4cLAfmkSGb+auANiR61W81gvRioA2Cb2BMQjHkSG05hGsfnV719U+hQuiCpxfRB9OIaiwiEoAb3p6OjISqWST3q9Xk8gjyDVmcx9Q71+v+8n/fA6MgkvXrzw+wkVLl/QkQkWoRjDriGLxMbS7JTWEcbKL3RTE/jB2OHzlaGhjcZQVoA4/H1dI3Scwu8xJfHQexgoIlBNssY4iazfYrFIZOoJWkAhOZp2e3vbnVAcB60LpOZwd3fXSqWSNRoND2i/++47d3Q5vm8ymdjR0ZEfw61GkJpFusMTJO7u7tqzZ898D85mM6fRxSiUMeX6c9hLq0aMcqhrBbBBvS7so1jtJEEDc8NzoyC3trb8lCCccPY8jCQNJPQoYCi/mvVjTpk7dUDJLvE6HMzZbJbo5h7rj6XPHzoEaa8LfycDqsZGZT+NBvlzR1gagR6rVCqeIdfmcjc3Nw5S8QUAzJoo0IZMNBoNP60AqjZzvrGxYY1Gww0YFOLj42M7Pz+3er1ul5eX9sMPP3iQBxPy/PzcvvrqqwSDp9Pp+HHbzBkU+/l87sfAQ2lFhnk2gLqQ6grYg6GOsQFUJymAxd9iTKIvHWmgQviz2h2SGdqonJNCtHY+1Lvz+dxGo5HrKOaKfjU893Q69flB3gkKAGkymUwC/MQxvrq6sq2tLavX67a1tWXfffedbWxs+P+vr6/t6OjI3r59aycnJ9bv9xOnyKTN0arfw/FQ4BW+5inHY8Ck8O8Al+rMcXoMTiI6msaZgG9KpaZclLW8ubnxRvhacmaWPFFJA3CGBkL6HkAG+klhF/V5ptOpB//sH+wwp7i9evXKzMxttWYjw0BC/cd1y+JD8pgWZIS/hwHGqv2GbtPkCMBnLIgzW/YCI9hDr2vPNv5PEMOpqdhATvS7u7uzUqnk5duUbFNWQdIFpgcnIE2n00Qjz1Kp5L0a8bc04ZoGVq0bdCMbriXRfE64p3Vgw0M7oAOgpNFo2MHBgR0cHFitVrNCoZAou1bbzAk0yLUCPlqarHob2SdgxN6ZLY9cRyezFvTVePHihbXbbbu6ukoAn2mgtVk6EyxtnnRfx/yjLx3hNTUIZWhpjNmybHo0GtmbN2/8xFFsET19OFaefUy8dHd3fzLmcDhMxCGwRD98+GDtdts6nU6iNQO6CvYO/ivztLW1Za1Wy6rVqvef0p5gyHnIcte5ZoRsdmRST3cjrozJVZqPoXO+zhGuYfh86tuYLZvgK7iD3QnjjtvbW2cSLxbLdg3oohcvXvjR5yQptfRpsVh4c2f61pDIVBZbsVh05hvxNoA1dox1orScMnyYrLGYSckG4b4OY7NV8TT2Q322nw3UxIKcmDCGD643og/KIhJQ0iAV8AVmDIh1Pp+3y8tLV4woR5wCnIZsNutNn7ShEMaNjRRSEXkvjpQ23Awd/1UOuSok7gk6KsGV2fKYMFDEMJhXZ2+dQ41MmqOJ4uP5w2djqDCyMZXKq3W9vBcHhuPVCciVQcD8Medkl1CmCoCYWWL+WFdVdhg8algBWkLWF44QDYzNlkc3lkolazabjtKHmSadP+5RHeZ1It0KEMbWhOchUA6723OfurcplaDJIYqTE2aoyaaXAYAG1HvATsCUYrHo9FWUH9fY3d31kgnmCQcRRBwUXe+Vn8lQPZTJCxUiI3S0dB5UN4WOxLoHMg7wSA10vV63Xq/ngROlBK9evfJ7w3kjW67GUh1M9KAem87+Vf2HU0PvEt23OKdKCa5UKlYsFm1/fz/h6CNzGvSl7X/uH0eVe57P585mLBaL9uzZM8vlcs4UAaxfFSDEgBIAvnUGFtqsU3Wf7lEtdyIYMjN3wpE9MomhXmHNAb3G47H1ej1rNpsJkE+fUcsc+F2dQxxMmB3a+wkghwzV7u6ug+0wNuhHsrW15WvykJx8rg4MA/xVTuovOR76bO41zNaRAYQtZmY+d2rXVBeF7NQ00Avbp/3bYkkkXoO+pun0cDi04XDo4KHq2/l87qAxLASyzdjLRqNhl5eXdnp66mBjGvuNn9fNxFCfIZyr8OcYmL9qXVcBdmZxnRIG9fqlTGFNdiodXxmR2HFtpk9pNr3l6O+A/0tQo+xkWCGUf5MwA1AkoOn3++6r6T76XND15wwSCsiExhFpIxYgh/eJHiYZSyZcQTGVn83NTZdFSuXPz8+9YkATdQpuxeIdZA722mx236iYhC9+pTZYHQ6H3ghVAavPHWngpCau1Z9+ypEWc4RzRcw3GAycdULCBjsI8IU/CpCtiXR671G+pOxGsyV7S+MO3ScwWDXRpawdfU+oV9gL+jf8J9hwNMNl76TtX97P95gcrjvOCD8rrCDgPpUEoUlUXo+eC8FD2i7QHJ/ToPnCV9re3va1Vp+KhCTzqv26SIak+SQ8R8heIVmsicswBtDYXG06602JHQmstDhNbY/+/4sYNQq8xAQaI8MG54HYhIoK8zNCeX5+bqenpzaZTBycwSGA6st1AYNwHLXJGhl4Am2CbehJWieuJVEgdQQsGMnQ6GvgooIZOqm6iZSeisOG0c1kMk7l083M11OUPqU5DjjnCkqZLamA+n7WWx1KXqtKj6BQnTIygaPRyKm65XI5wVjKZDKeVSFrrHWc6pRCP9UA4/b2/hQKEPqzszMbDAY2HA5dkXOfqjjoKA8dlbKN3d1dMzM7PDx05FYbL4bOpwI14ak56xh6KkEMxTdbgqHstVWKX40jqD5ZIGXIVCoV368ED9CGtc50b2/PHRdA13K5bNVq1dFq3RcAdrlczqrVqpVKJZ9/BU7Mlgw15PiheU1z2FTZh6zAEIAM52tdA0CE5py//e1v7Te/+Y01m017//69tdttm06nXobw+9//3qbTqU2nU+t2u94AElAN2dBMProPJwUHWLPZ2oSN+WUNlZ6by+W8N0W9Xrfd3V07ODhw3blYLFyez8/PXd5U1+tcwiS5u7uzbrfr7Djde0pn1cbgWmLAHITBVwjIaXnHugaOAc4796ENKLXRHf12AKfopaWAPToldGAXi4Xrso8fPzpIhlOh2XOuQ28oMkDoQz776urKT5wgg4TOxzllDxHYT4Ib6s0AACAASURBVKdT72mloGtsxNbkoaHPHQuqn8IhTRthgPjY+2e/hUANSRvmGKBG5VdtoQaEsAFU7ylIiB2kEapmijUYhe14cXFhw+HQBoOBdbtdb1IPBd/MfF/v7Ow4EI+e1kDm2bNnZnbf6L7X6yV8xXAv40M9FHx/7lBgPfTN9Gedj1gQoiMN+OH38G+so7JjYnZKk0qsN6/RfUPQgs4gKGV/oU9oYgsbHNYidg75HgwG1uv1PLmC/8tpcvib3W7X7u7uEg3c1c/QeVk34NZsNhNAoNmnh1fE7HosIIoFyfSCgeWuIBhlGBxwgk8Lc3gymbht1RJ4mFHsLwVKAbgB5mFQwdSBcYVfxAEA0+nUmanql3zOiO1dXbdYrLOOEVsjs7g/pq9HRwFcdTodb2lA/zoqEsJ+NVdXV4lTMemnhV9DkpA4EZ8kPCQEgBQ5KhQK1mg0vKGx7hP2lcq56hrVPfhWVH7U63U/qINeKuF6hQBpWvzG/9Yti7pXVAbRXeqvsI801oslGFRmsDcwBhWUg43G6btcj32rrU/m8/kndjZM5OtQBio6JpNZJi3BEMbjcZREwP0VCgUvM8XvQ3eS8Ai/0vSX+qoQOmLjs0uf9AP0f0wiH4ahUMWmSh+BGY1Gtre3Z7VazZ1/ypZUgVJDCupKeQuAC45lsVh0wGM2m/nft7e37ezsLFEuAOuGjK7Z8rQTJpfnigXkoRMCi6BcLvsxydpUEId5NpvZcDi00WiUWCjoXesutwidlnDzhD/zHp4tLeDnuUEjYUQgRGQTCEpYT8AaM/O1vbi4cCqogj/sG2VTsFbq7GLsOK7v/Pw80WchZvi4Nz6DfbC5uWkHBweWz+e9j1Kv17N8Pu+lQty7ziuOFrWL61xH1kgziDFDDiAFoJJGz+T9k8nE8vm8zxtGrdls+jNiDDnRyWzZfFYdDmT26urKdQHXw9FR+i8BZrlcToAKWlcPVZH5DFlouhdVEev/yB6q/IYMJZ5DX/NznKSHBqyjZrNpf/jDH+z777+3ly9f2vPnz+2HH36w8Xhs7969szdv3vjJYzjco9Eo0TyWASCgRw0qq02buDEXWj+Pru52uzaZTLz3AawWnFqyVvv7+5/MOe//+PGj1+uamQMzi8XCWW6j0ciBAwDSVqtl33//ve3v71ulUrF6vW6VSsV2d3dtOp3au3fvnOas+yAtkMLGPEUJG6cGUnqA00cjPDPzUhHWQUto9bSd0Ckz+7S8EQeHIB4wBiabnkAAgEfWiRJH1oZ7he3J+uE4ooeLxaK1Wi13JNlbeoymZvp1LVQ/hcF6mi3Rv4ev18D6lxirQJq037HfnGKBsw/ov7Oz48cAd7tdm8/nzmzQQDzcB9go+hugZ7U5Iu9RRpcyUtGnyF+/37fT01M7Pj52e6G9QbTsEru6s7PjZQnol1wu5+y34+Njp6OHPdF0ntT5XdeIBSrqo8aAd3RXuMewAdx7+F4tMwUYUWdee5FgtzQIAXTTshplN8K8w74C6sH8NTNPcgDI0p+BoJLnoP8iLBvAHYIr2AiqHzXACdcv9CHXOV68eGE//vjjJ6x29fViQ21QCCQRfFUqFfvuu+/sq6++smq1ant7e4n1wZ/TPkDYUU0QKxCgJf4EWbyvWCza1dWVbW9vu9yhP1n/i4sLP40UWd3f3/fY4OjoyH0yTSjFxmMAF16jCdankMXw82KgQxqYA2O20+k4SPzq1SsrlUoeV21sbFi327V2u+1+xfX1tfvn9LfjBKjnz5+7D3V7e+tJK+QN8AT2FOubzWY9ybixseGnFLF2xB4KepktwUVkGztdq9U82aVtH9LWNraeoY38uUDe5wxNnOq9aQ8mPVFU/ZS0e2StYasBjsJ2I3ZANmHdo1szmYz7xOrnKRsOHEBPUSPupIWGmbmfyh7D1qITIALo82qpKl97e3vuY2usos/M99AWZjJLIkva+OJTn/S1PDTKTU89Cg0AgSQZvevrazc+0GzpY0D2Dic3n88nAn9qQQkcEUQy+WSElI0DSofhMksetRw6+hjwUDDZuGpwMRAKPLHAODdXV1eeWQ3HuhFS5lw3hv6Nn/W1Ibqrr9drEcTpOjJwMhVo0wa/CBzsDKUEc20MJNfmd8AY2DiwtCaTiYMKejpRLMDXZ7i6unJHlIadun6FQsGPjyPQIpuCAUSon2INYwFdiFgz52RQQwZR7HrIIccCM/c8c61Ws3q9nlDYYa09Dr1mGZAVpfTRjPT29tZBnN3dXa8DBujT9Q8d5lUGLeYcIJfFYtHlEFRfy8NCXfcY5+fnDHTjYrHwZoa7u7tuvGA4oYdwxkHr9eQQLU/C6aA2l3vH0LG/Cd4vLi58Pnq9ng0GAy9toSyR+ZjNZjYYDCyTuWcuXl1dJUoc0avz+f0JJNSXhw0b7+7ubDgcejkTuhxGR6lUskxmWX7D57VaLTeCZktQIQQgQ732VA5MpVLx+nXNPr948SLRqBBqMwzA0Lak7eUYyME6qGwAaN/e3iYCNLVT6AJKHwgcNeBGj/JanBJ61yDznMzVbDZtNBp9AjatAmv0GVcBNzHbk8Z8+L8wuN/QOUe20JPq60DpVkBfv3hu1V0E26p7tWRDr6NJAgICdIsya/SkmRDIZO9ms/f9TTY3N/1UTDNzG8G+aDabrle1RJF9ix1Yt0zG9kXMRirIkAa2xfaxrq3KFYEJCUf8zjRwI/xMZJb1IhgA5MH3AIhV4B1fkqGsca53d3dn/X7fer2edTod73Glz6mBUBpjNzZPacH2lwyeMdTdMZ0SG6pTFDgpl8t+Wh7AC/LBmqluVuatstxUZ6MfAeD53FA3a3JDAQD1OQjkYTXt7Ow4mw1wPTwQIs1uhPMT7sU0f3+dI5SxcL10nUL7powLWmJcXFwkEhKFQiHRxwc/iD0MW0kDZeI8tY2qK3UAPCjIbmaJE/ZITsOGUnZS+JkARgBNhULBwSXVC6uAyLSf03TLlw59lnC9+L+ZJfwI4naNZ8MyUN6rsqSyiE3ThKvKM3qR64RJaGX3sBfYH8pyZajdzGQyidOclO0IAA8ISzKGioDFYuHxLAxkLUVWwkGa3D20jg8CNSpIYaaZidaAXYUP1FmVodkSEMH5pkcGgQlBHMgWnwMgQ1PGy8tLzwxyIgKdlzUAxCECIACJQ5hA73AqFGnHCKuDy/9VKKlf01o3DfRhCVFzSZ1e2MSU661z6PVV8GKIX/h7TPkr9U0Dgb29PatWq4ksh5bHMP8K1ICsAqiwT0KQRvvMzGYzm0wmTs0HaBkOh9bv971JIq9XIQkNlf4dxghMKChu7MdWq+VNy87Oznwf4Tir0gmVwrrGQ2BpTDGsMs6AaXRiZ6145kaj4c2/5/O5B3Jmy6wflFV1YFGuKOv5fO7gGawqGsRBMdUggHkMg7qHFJ5mNHB+0QXI5XQ6NTPzTv7he/Vv6zaCBNS3t7fWbrctm71vKvfx40czuwfO+v2+18kqUKNgKIwNdBhMh2q16iWkasxgmAEQkcnI5/N2cnLiR2tfXFx41p65vru7s9PTU9dh7AcyirCuzO5BjHw+b/v7+zYej63T6dhf//pXm0wmXsKjzfMwogD1MFM0OD04OHBjuFgsEoDNQ0Aka7jOdaQp5f7+vnU6Hdfrv//97xM91NBBGxsbNplMPinFemzAw37XcgTNWvH5SsNWhxT9OZlMoo1rAQaVfp7L5TxzyBf9iA4ODqzb7fp19RlCeX3IAU0LmsNnj/3vlxixZ4gFSqFDCYiBX0TCiawtPgTBnjqq/B3qfqVScTYG+kkz/CHbENuJDJPYgMWGr8RnxkqCtQyLkhTYpfyfZy4Wi/b8+XOXTcDL0E+IlQN96dAEQijnITgT7klYTGnBL3ONnlX/DzuHH8vzKeClQT/+kCYukWeyt7lcztlXJDH4IltM2RLAjdmSSUUAiF3++PGjtdtt+/Dhg11cXJiZeTKEuSDAVeDuMSyLda8jezssSX7M54T+DcEj/mij0bBisZgoCdV1D1mqrJGWFZpZQi7Zywp+qQzjF2HrsPuACuwTyrJo3N1sNt2Wv3//3pMYrH2aLxf66A/5iY953eeOh0CHhwbPSHzA6XesHYxFSqBU7pAn9Bo6V9lSaic1yaE+M4xjAD5K1Uhi0QaBMjiYamZLXWRmnjyj+Xej0fBybspUlZWxah7TQJmnAmrM4ifaskZ8KUgDGULLdxSYjF2fNWD+kUutnEDe+CySmsitJgWIUTKZTCIRy96Bhaq+reobStSo6tDn2N7etkql4v3A6Ker/cAgg2Sz2U+Sqg8xZjRmjo0HS5/CTRMGgPP5PDEpZFqy2WziqDoeJtx0oKPD4dADBBx1bpzaf216CfAym81cKfO62Wzm1MLr62sXTl3obDbrJ6NAZ1MUTo0/iJpmLhS00swH6CnOFc/OBjGzhGLRYJQsJoHkuoYq9RijJgZahO+Pfel1VEgR2vl87qcv0T9mY2PDhsOhNZtNnxP2C4YI9g1GEEqjNj1UQccw8l5tVI0jq3s4LbAjUOH0KbP7NaF+en9/3w4ODjzg/PHHH33vwzhQoOIxQdjnDPZi2nOwJgpo6P/SnpmGkK9fv7abmxvvc4KDCD2Xbug4G2qUlE3GvtfG2pxKAQA6GAzMzPx1OJtk8DGaGO5er+dMDM3Wqi5R54v/oeCfPXvmwNDJyckntaQ4qOicMCuzrkE5D6V/R0dH9uHDB89WM8/j8diVPiwUAjz0Ksy9/f19++GHH6xerztQA4iCPJycnNjZ2Zk7EGHm/ubmxnvgwIL59ttvbXNz08uuAJFyuZw9f/7cWq2W7e3t2WQy8UbCgHzPnz/34GZ7e9ufRzNe6nyenZ056KEB6vb2trVaLXfkzcx6vZ7LPfsgzakIGQbrGPSEyOfzDkzt7u7aixcvEsy/Vqvl+m1zc9OOjo7s/fv3bmvS9IPuZX0+/T8yY2YOmAASUOZHcEcApKXD1WrVX9tqtRLN/ZDPm5sbB60B+XZ2dmx/f98bj/K6NMAsbaQFGqrDFMhaBc6uczwEHPG3EDxiTZSNiSOOzsO/gM1GMBH6G6w/QQDliOq4ai88fCtNTAGOkCAh6Dk5OXFdyrqFMsN3fsbOkjThedCVGxsbdnh46JlMep4pgBv6HOscKitpQY6+Nnxf7H50P7MW2ETAVsBkrgHoXCqVnCUM+3BnZ8cODw+tVqvZ3t5eAmwh4UTwg97ENpOkxI8l4OS1WnqAnPd6Pev1en5KDn4zvTJ4jtns/mhk9sX5+XnCNugchmDAOoeWTsTkQcEl7kl1vsoGLIZvv/3WQRpl1GAXWGcNtLmm+vyZTCYhRySzwoy+AqbcY+iv4dvc3t76iX8EiOjaUqlkv/71r70k509/+pMNh8NEYlDXILYuIQij+lT9w3Wv46p7C0fMR8YXG41GZrYsM97e3rZGo2HZbNbK5bKDICSA8BOn06n1ej3b2NhIlCdfXl56+SBrhDzU63UHshn0wkSXf/PNNzYajfzEIsqZ9vf3/Vhn7CxyrKf/EdxrfKNNkENfMwSVYwBdWrz2pSNM4Id7hoF9Ub8fX4FDEmLPBiijz47M6X5ROcLWVSoVvw9K+ZEnjc339/cTJ9+ii9F7JB3xsUlOoeuJ/wBpKKmLHc5hZu7jqd0JnzmUTeYUW70q1nj0qU9pQxEvM0vQh8rlsje4a7fbUQYJTuZoNPIz1HGCMXxkhQqFgp2fnzswovR7EEoEkfdq5oGNc3d35+9nctWh0kyQ1qNqtkznB4GmozdZEW2OjHHA+CrVlE2qAehTDd1Iac5NTEHwc+xLNyybnHXr9/uJjCE0aNZnsVhEN78a6xA00/th3ZR9Q8AYUslXDQV/KNlQphSleATTUFQp59FGp7r/1jWUop6mnNOCjDDoCZ8b545sKA1DkeVms+ly1W63zczc6WF/a38ezTri6ADaKctJe9Ao+KnrHCLvSumPPYvuadYAIwmYqhmxMDB5SieGgSPZarXcYeeIVU6FoBSt0WjYmzdvXG7CLISZeRCBU4M+gZGoe3NjY8PnA4BUs+rayFTnXhvfTqdTOzs7S5Sr4RgROND0VmVC9284cLQmk4n1ej07Ojqyq6srq9VqTiGm4SIMIS0/TJMHLTVb1zg4OPD6dfQBQC76gF4k5+fn1uv17Pj42E5OTpy5pnaQe9UR7kcCZQ0IsUOZTMZZEgQYOKGaaNBARjPDWrJIIDKbzZx9wSlPBDuNRsNevXplm5ubDrAB8PyceQ6zhKu+/5Ijdl+r9pn2L9GMqcoVcszAcceZBCzGHuIocg1khAQBgC0BDP1KzJanPWl5i/ZHUrudFuzxGtYWJh9Bie5FvUdOlOr1ep/M1zoZwwTeqvc1yEn7OaYzQj3BnAD2w7AlgKdBKWsHWEfiUJkA2vyb59fP0gBBk3uhLeV16tizx66vr7334fHxsR0fH3vQhD0giMRWhwzyGEgTzlHs7186tFwwzKgrSJTmo+I/AHTU63U7ODjwxAjgtdoDvtTnv7y89LlBp8KoUblhzfDbkQf8UJjiMD9gVHLds7Mza7fbDpphO2lS2mg0bHNz07799lsbjUa2ublpg8EgcXQ3I1yvcL/zd2wB84B8r3OEAIOuWWzd0sA/ZXoR0M/nc9/D1WrV5vP7kyIBM4k5tOzMLHl8tNpSdGEICGp1BFUcw+HQGeGwNJBFfFxlk8CUohQcQD2MaVYlOMJ9nyYLTz34TAU3iRvw68zM9Yq2E0m7T56Z6hcam4el/mFCcXNz8xNWIboRcIV4huoNLXNTn4o9om1T8K+0NBxWI/1tWHPKVLWsCp0Ni06ZWiFYo/OjTKLYeJBREzOCIbrORJmZ1/eVSiXb3993OjuLqfWWsHHy+bxnZGnARUBFtvLm5sZKpZKXlYDiITDaXI9F1WASQ4ay1dNOUFYoWu4NJ1YXhmZDGEeociCnpVIpkXFlY6vywADgRDGX6jitc6giYsSEPebYPMTACbMIbFg9apn116aJGsRr/X0oVArcmC2PONf5jwE1YTD7WLCGPQB7xGyJ/HOfHFVMyd1kMrF+v+/Hk3Kfqri/dKij/9AeSVPgMUeUZ1bHYjweW7PZdCVlZq6gmGsFWVSxKQVSnUsCPy0ZUzCV63Bf6gQpKyI0bKueFflGwbI3FRQyWzqEMZBm3cYQZw39iKOG7oDtgkNQrVbdaGnWVA0YOrJQKFi5XPa9q71RFNQh608wqFRsNbrMDYYPuaOE4+LiwiqVir8ep5hSiM3NTXdyQ7kOx2KxrPPd3Ny0Dx8+eDksYA3gFkANJ7al6c2nAmpgZ9E8mD1Gdo356Xa7Np1O7fj42N69e+dMLlibusdCB1YdCnQSYBnOCU6rmbnTOZvNPFFCUMlrcSy12bQ67qytlk+Q8Z3P556IoCFisVj03hc/h7W0Snb5HvM3funxmPtUPahOm1myLAKbpbpHgRqCTt6PbJNAILGl5eIA6pVKxRuWaimx9kxSHyhMgKSBNloapSVM/EwCg/u4vb21TqfjJX/4OBpQr2soC0L3SRpQo8F/yCYK15TvAC2lUsmZjzwnektLBvWkNxg1ZOY1iFE7g0+kbFQFaDQIYO41YGcf0ZPm3bt33t9EA0uzZPN5LctZBaTrPK1z/Rg8H/4DeyX8bA0a9W/K3qxUKtZsNm1/f9/ZS1pyynzxPk1mAGpls9nECX1my1P00HOsN/KPfMBiPTs78+CexMJ8fl8CDmu1Vqs52xibTMKFE2j6/b7rEvZUCIqr/UjbzwrWaJnIusZjAYQYQBoOdBf+Ir41/hOMUHRZr9dzeSLW4oRM1kdjCT0lKvRJtEICvYgtB5A1SzYPB1BnT8Ai17L7TCaTsJXqEz1mTlet6zqH6lD9DPWRNYmn7GAFaB4Ty2azWfdbYSdj68yW/Qh57ebmppefaaJJ+zyRQNcyY4YyubU8UWWKa5otSRjaYFx1lSao+dJ4hsRM2jqFAGHaeFTpU2yTqOEFpdReF/P53PvHkGnM5XJO92LC9L0ANXpsLCcjMAEEjAgli0cmiRIpbTiFscXgbmwsjyYsFosJY5vJLE+E4XNpqqr0qjBDzclVBF+UcUGRvLi48NM3ut2uz5NmeObzZTnXOkfo+Id/Y4SCFWaf1JkL9wLrqE4dxtPMvASNfjbag0Nfp9kmFIIG0gipBhmhwTazT4xZ6HyGDpwGK1rrijMGug86TjDKiUivX7/20hw97WxdY5UDFRsanIY/hwPFdXp6are3tx5otFote/bsWYIiSo8RbSCM46cOjK4j73/27JnPNZ9DgAGbBFCHOby4uLDRaORAWBhM6JqzDxkEEFCfCSa0cSEjdNp1761zoN/MzB1pQA+zZRkBeuUPf/iDM25evXrlx2Df3d15T5p//ud/tt/97neelQOgAdDo9Xr2/v17MzN3AC8vL11fMvfI13g8tru75fHZ2uwbZtR0OrVCoeBlq91u18uiOMWJstRnz555g7XJZJJgRalewjbwfMPh0KbTqf3bv/2b25Jareb7mUyXrlWo056i9Omrr77y5oCNRsMBR4CqSqViFxcXViwW7dtvv7W3b9+a2b3Rf/v2bSIjFQMjYgEmQYiWPDCHt7e31u/3PSjDSeQzoX8XCgVvtm5mHmjjIG1ubtqvfvUr6/f7ThvW8hZ0LewNMmHb29v2+vVr++mnn1ayasK/xwBXDbrDpMA69WlsxBzhh/6v4Be6Rg8PAFTLZrMOyJiZO4pkg9kPmp0DRB2NRh6kaH8hQHSo2a1Wy+8rZLLFTo5TG4reVfAmtO2ADoBNmoCDoVer1ex3v/ud96caDAZeTqNJmnWvW7hfzD7dM+F+0j0Xgg9qy/AvDw4O7ODgwNnS+HCA2Gb3IMjh4aE1Gg33a5FfSjbU2SeAxFYBkOKDEqTMZjOfYxg1XB+9PZvNvBfZcDh0H4RgqFar2atXr+zly5fOtjs9PXXGh/aR1HnVQEP10joHPr/29UsL6MPPZ09tbW1Zs9n0nnfZbNabnl9cXPhnaKyg+3uxWNhoNHKfRUu6uT5roUz40I9gXZC5drtto9EokaDVpG0+n7ePHz9ar9ezg4MD+/Wvf+2BYL1et++++86q1arVajX785//nCjrf8i/Df/GazVZvs6h8pU20uyc+gP44MPh0MuFVTeSGOJaJE2JszY3N91Xx+9gvReLhfehqdfrdnNzY8Ph0P1/1gZfyszsu+++c7BmMBi4z4RuNTOXKdYO2aFHqrZl4PAGLf2JzVG4hsogXJWk+pIRAngaq2sCneQQ5ez9ft/K5bKzVNRXD0E8rgXwqAeZKNDGPsWO7u3tuY+KvkIX4oflcjl7+fKl+4+TycSfRVuQEAsgrzxnpVJx/zmsENrb2/N9hE3TvoTYQ/RK6NfE/Bts/6p1fBSjhu/hhzLhBOeU9dzd3bkgcXoHk0IpFIvCJGvPCBYqn897pgIDoiUAoG1sFmU0EAACzCAooOJqhELGC5sDZwsHSjPULAiKH2qU0jfVAVMDSuATZrSUhrrOEYIzsbWN/T3cTLFsOE4CAjEejz1gpMae/aEIJoIGTUxpdeHajkYjd/hwWFV4cbK0wWI4HgI5VIGgfNVxpcZ/PB47NRWGF0fVhnTkdWaeHsOkiT1TzADE1hCFc3Z2Zqenp3ZwcJDIyitFcWNjw7vfxxwmdQL4PLIYKGXKRQh4CWq04bTKYhqbJu0ZyU4SPOlpbKVSyU+Dizk7DwVsXzIoqaNvgJ7EgpGfz+dWr9cdSN7f33e9ghxwlHq5XLZms+m1tzg4GJ5+v2+DwcCGw6E/S6VSsdFo5CApmaL5fO6nGdTrdcvlcjadTu3k5MTa7bZtbW1ZuVz2I5wp8xyNRjYej+36+jpx1DrreXBw4ABfKO/sP9aYQc8VGq/jOBcKBZcren0QpLBXYiD0Otfx8PDQGVAc34kTQZkKe/vq6spZTloqFLL9VLbVIeL/2MjxeGyTycSPfGUAKmt2GFuJHVNnC/Ycjgg6F8armXkWV7N/ZKvQu/V63V68eGE3Nzf2/v17X8fQYX9o/mPBn9qdmJw/5Qj9nRgQoPoC0FqbSOKsA8gpexBmlTIZyf5zTQJBAGx0NMxEGpVq0Gm2ZFcwb3rIQSy7F5tX/obeBpzleZTGb2YJJxmdWygU7PDw0BNmZKPXDdTEQLw0MCHm58SuFa5tyJii7xZyCeORoF5ZKlwnZEQyl/gNIXBGMkTLorg+fhDBjgaClF6iDymNrVQq9uzZM+8PSFIQ3ZGWDArnZ92sKDNLlKqHwHWYjDNL+rS8Rks4STRMp1OXMZr2EvBq+Q86luTQxsaG60/kVmUXME2BLPSj+rij0ciGw6HbP5oD81o9tZZkRK/Xs0ajYbu7u943ZXt726rVqu3v7/t9sP/CdQuDQZ23pwa8f46ejgFwyiKlbKzb7TrQSc8SmA69Xi+xT1hPZX0r440YQu0lsSNVIdqOAWYUvis6FZ0N+AOQCsBjdg8ioaspeyOWiQEtabYyBLjYe+tezxDQ1r+H3xVs4VmIsUOdotfSBveQGcANFOzCJ2QdVf/wWSSw+ILwgB9MjE7pNgwg7C96kL2hzYRZS8Uv0Mka/4R2g3VSUDcEanSENjkcjz6eWwUwdIQBPtRRzGSWnZd5YM0ihM6fBkz8ncCebL068/xfj2xmwck8QfHP5XIJJDuW9dPPUxAAKhYGGmdHnSCtYSP4UYMCoovSZl5UQEPEcp0jDARiAe4qR1l/1o2mQfjZ2ZmNRiMbjUbe9GuxWCSAGtYJlgTODQqSz2EtVFGTITL7tLs696H776H50HlQYVPFQ+B7cXGRaDZVLpft7OzM6vW6O7IKvnHtdTozMbaAjjQDGcuApaH1GBJOzYLuTdac7PzGxoZdXl4mKNxmy+P6mH+9Z8p5AFMJwmu1mvdVwenV+lL2zkNOpP5PaeR02gekIYiAXRPWUX1AmQAAIABJREFUaseM4ToHoMh0OrXZbOZOH8CXgmIAJ7VazSm0nAiF88LJFjgfOOsYH05BoxmsmbmcksGEPbFYLBIMwtls5hmtk5MTq1artre3544ogT712/P5skEfIEKxWLRWq2XtdtuzWtSOh+upOgowgFK8TCbj64fTTCaL05WUOaOysm5Z3N/fd7nQXmnaf4J7mM/n1mg0nI1wdHTkukJZd2m2lb+xV2AjAiQrG0KzS0rRV6o798T6UVJmtjxZTFmoWnKJ40XjYfTD119/bVdXV14Okgbop2XEYyMER34JkCa8r/Cew3tQXaFBIk6dMgQXi2WWHqYM/gbzOZ1OrVwuJz5fAwscUy095PO1Hxo2jKCX0jzYyWl6Lc3ew0ZQoAaAVPs/KBgFqNBsNr3E5vz8fO36lMxlmL0N5UcZaPr30BaGekPBEwVTAD61J0k+n/dADluoNpC5V3Alk1myGxQUUgaSgjQ7OzuJHmv4tJqFRr9ghwEPK5WK7e/vW71ed3ur/ndMbkN5CAP/dQ1lEantTbPDoTwSODHnAFY04T0/P7d6vW6VSsVtnII9zANAipaEkSRhXbHXYZKHNUBmSM6SyKBcUfUwa05CcmtrywaDgTOIOVUvn89buVy2VquVAG2VuRD65zGd+ZT6NOZr8pmr3hP+rH44dgqgBnAG5ijJG3SvsrnRT6p3tQw4lAHkBX2prSs49Ymyf40HuW8tb4RFw3xgs6fTqZfUrSo1ZD5iYBtD9e66R9oeCTEBnhEdpICNlt2H+lVtHzG5JrqxIbxPyQ8a82lSSfvDEGfARKSHI+APQI32ig0rAfhcBd+I7xWoURyDvat4RSyuj+nZnw3UMDAKYbAeA3DMlqAHLIu7uzsrlUqWyWTs5OTEHwhH8u7uzh0PgBCEEMXNpKjRZHL13Hto3lCzeL+ZeQadz8RA8bsqWt6D00XmVI+Ew2CjyBVhzeVyninrdrv29u1b6/f71uv1nKKOotYFUrrruoYKerhuMQdF38PG4z2hckBYySB0u10rFot2fX1ttVotceoPc0b/hhCdJCun9dR8BnPP+hDMI/DaQyOTySQcj4cMBV8ILM2EdV5CJ4V7NTM3mnot7VGwjqEKL6a4wzXhb/z+0M98BnXBHBeNg29mTgFH0TIPGDaULJ3ZyT5B7a7X6/7z1dWV1et1q9frdnh46CANdGGcTQJ1PaYyzREI50DLnmB5EEAp8ycmEzEGzzqGZj/5wrAwRqORtdtt+/vf/+6sRMqJvv76a9//ZPTI4LMugDWj0chOTk683BLZLZVKNhgMEvqKbO/19bWdnJzYycmJ9ft973cwGo3cudV+YJQ+9Xo9l8PpdGq1Ws2NJk2A0e8EmOHQeccx6na7NhgM3EH75ptvnEGAw10oFGw4HCYcH3UIzB5XL/3YoQA11w7p8OjKXC5nlUrF/uu//su++eYbazQa9t///d/25z//2T58+PBJ0iCUSa6j9oX9sLOz4/p5a2vLQT+CFJxBDWRh5Zydnfnf6vW6Ay+UqV1eXiaywLyXjBQMOMq7NjY27C9/+Yv99NNPNp1OP0lChE58zFFZBZTE3rPuEepP/Tmmd9B/rAtAiJavaC88PY0NO0O5LCWz7B/WW20LehZqttm9b9ZoNKxWq3nTZxxfZRsr65l7D59JfZFw3ilrB3Chn1Qmk/GSdXR4sVj0Z+z1en6vlJavEzRVmx+ujf4c2ztpa6sBiJm5TcL5Z420PwWv1xKnvb29RACvmWPmhKaj+EGz2bKJNwE6WWYNGtAxgNX0VwEg4O8Ep9haSvPwcTjCm8RBCFKxXqsAy3UMZYlp+bwODaJ0vYgN8Pk5iU6BDPVbaeKvDb9vbu5P7qT59fb2th0cHHhcgR2G/UI5NowX9AD+qYIGJE4ICsN5RD9TxkvMQf83np1ydBrXZ7NZZ+sg12Esxoj57NiOdY+fs0dYn9BGkAiFlcKAGbWxseEAFswWTVJory8NopVdDduVfTAej21zc9MZu7lczr7++mtv4j6dTv292uMUXY/c0CPl6urKBoOBdTode/PmjZ2enn7CekubrzAO03li36x7DUN2bwzcYw8x8BFCnCAEafQ1vB/ARAHr8BmV4UJpeblcdkYauhW5/uqrr6xYLPo860m27K9isehxivYOI1GlcanqXrNlrIRtOD4+tm63a0dHR17SpuVcISs4jL/5f9p4NKNGhypxMqj0n2Gilar3/PlzWywWzowwuw9GNFuuzgROOM4jQT7GEiBHaw5xmujt0Gg0vJkYAMHOzo6XHGjQRsBjlmRroCiUnooTxcKGnZ9Ba9nIt7e3HsxowKngR2xen2LEAvmHnFIFusyWRhFKIMJESUe73bZqteqZRZgTZubUtnw+70Yrm836sYQgqLoWvE9PC6K2HzohQoGjRPYI5lIMaNHvPLMKlP6d72pIUOoM9gb7Y90OaRoqu2qvhOv5GAOqznzIZNG9j+ypM8fch3OtZRXaeJHSHZqF83nKfAvLZMIRMyCZTCbRwJEASRHwtCwU11g3tZtxfX3t10av6POp3ri6uvIyGdYP3aLZBhxOzWTgiAOAo/eYW20qqusKbfzu7s4+fPhgnU7Hm+CyL9QJ5jMAafb29jwYQX/CbNKjbR8amiEfDode6gNYSGM3mEiaZQ73g9n6T5rhHpEPZfipjmQ90YfNZtNevnzp9deUjKkDr/dORn13d9cDck4G0ZN2sJnq3BLw0fAcfck+wYnU0rejoyM7OTnxrO50OvXyGpg3i8XCAQP6QWxsbNh//ud/2s3Njb9/Vb8anm+VDntqexh+ln5Puxd+JjDj2FjWhYaSvFb3JT4N5YIMzSaS2CiVSl5br2VLOKTst5CVDEA3GAz8dehmbcAYykrIGDFb7kX2GCW+ZLRLpZKDqovFwvWBNlSEhcPv615PDcDTgMCH7KRZEswJr4NMIaPYF00yKfVeD7sA5OI+uY7OAz4jPhX/w58Jv9D1aqcpLQvLr/GHOaqY/Unvo9Fo9MlprDon4Rc+r5Z2rGMwh9iyNNCI33kPPoc2+gSo0pPoFOhGf1JGs1jcZ9cpsZ/N7nur0Q8NmaW0BT8sTBzp/CFD6iOnJX9CQJCSEPYRsprL5bykMJvN2ng8dgCDXlcaPMdsIbL/FCUzulafc+2Y7g39SmQNPaJAmL5W+4Kxp/AzObZ7b28vwZ4wS5Zusg/RiVdXV9ZutxP208w8pmQfKICqgTkJR9iwyvZYJXP8rjFHqD+fwjbG1k+BmVBXhvp1FSjOz5rgpf0BPg2fpbJCTLVYLJw40Ww2rd/vJ/Y1colPOpvNnMBBSw5Yj4oZaIyAzdT9x54yW8ZiMIJ6vZ59/PjRRqORJ16wy2HCKpwTtcVfDNTEJpwL06Wc8iSMEIhwrVazZrNpmcx9hrvX63njMrJ6StnWulCtAwSoGQwGls1mEw6ROhgYS5xIjqo0u+9bQ3YKQ0oPGp0wdVRUwfLcIR1Kj+xS8AgqGKwAENZY4PnUzmi4buHf1EEIwZkwgEVhsl4EBmb3QSPHCWI8oRGCiEMRZn9Vq1UXFjJBZsmASPsP8TuGmKARZUmmfXt721+jI03xafAevp7v+l7WUY2TOhfrBmpiQEuaExr7+2MMKBkJRbLVMMG+oKGhXhuKtjrPfCbAA5klmrHR1yOTSXbEB6SJAZrh84UAHM6k9o0KgRoNsEMHPc0ormOEZX7h8+levL29bxLLnAN0qvHAmWfOeR8OH9nvm5sbv4Zm/JUerAwxskCwcVgTsyX7EGqyMtu4rnbbV+r+58yngsBcP5fLuXEvFAoJ1mRYoqDzuc511DVSR5yAAHnRGneze/nZ29uzg4MDB++VlRM62azz9va2FYtFazabVq1WPeDSPgpK6TazRNCux8SGWSuy0IDdx8fH9tNPPzkTiwCgUqn4SV5k+ckqIvv/+q//akdHR86m1WdK0zufsy5PbSPTPiu0nQQEsJuazaazKFhz3kPAQKAIWKOJDi0Dp2STgAxfBQdW9aLanPl87mvNiTLcM0wA7ZHD56tfo89nlgSxSZYouAtbGiCV/gv4hdvb287CQjbXPTTDrEN1AI53+HfV92FWWl8DEKK+m66rJi/wDfliXfkM9KiyVMjEQ8vXudLglM9TYF19zTCpwes4Knx3d9d1PMw6PXI4lvAI5QAfW/tjrWNoolMDIl0Lnom/8R6AF9oUYANDdjyxBKeyKrOWxrWwWjY2Npw1rI2OYa1pzMLa6D7Uva7lD6E+1H2mGXrYp+E6c88c7nB+fu6nHzFi88bvKuPrBmrUv4wF7qsAhtBfDH2wMKDVL16PDcQOA7pgP7GZ2keKa2vyXdt0sCb9ft8ZjwCwvI/1VT9M9SYxICzhsP1G6P/Ffn5o3p9ihHp1VVy06m+MMI4ERIPtgl1kTTW+UJvEetbrdde5qk+Ju7GT9KcpFovOulHAT0uWNckei+GUmXp2dmbD4dDa7ba1220vSVawPG0ewr390Fo/qvQpnGB1Qg4PD+2HH36wSqVif/zjHz3rksvlrNfr2dbWlvV6PT9dSZExvf58vqy5hCpIHbQKIsqYzBKBCpnWUqmU6G+CU2FmiWBBQQCurwZSgz5Fstk0GD1FUXkPTs3x8bF9+PDB/vGPf3jmNARpYmDBuhWoCpwqzViAyvPxd83yaFZnc3PTy40QKrL4vV7Pa0eZ80Kh4OwlAkkcHz2yXbtsq/He29vzOs/5fO79Maj/PTs7S/Rk4DsCqUCHPqcayhBo0fnS9YrNr9nSQGq94rpGqAwfCkBDoxgzlvq/EIwajUa2vb3tTCaCCMA0DfTMzB3zzc1N29/f99cgUzSiVccGGWRvoYCHw+EnjUwfej6+E0A1m017/vy5tVotq9frnkVRPaMNvTG8Osf6fOsaoaOkwb7KEgr/T3/6k+3v73tzZ46Hpe/DbHbfR6ZUKnn/EE4VOD09teFw6HXAZpZo6A3bRptUXlxcWLfbtXa77Trr+vraHfNsNusnUHBaUK/Xc8PFmrbb7UQjd/bsQ4yaUB6V5ZbJZLx3TT5/f4IfTnaYTda5DO3Nlw49+pjMMs699gXR++bZFNzHYUBHhY4+QciLFy/sxYsX9tvf/tYODw+tWCw6CxUAlMADnQZVnwATBwUgdWdnx5kgnJ6Bzb29vXWdSrAzHo/tp59+stFo5KWt4/HYstn7nl2LxcIODg7sm2++sdnsvjcSc/RYGXpIl63bLj7mPmKBAfoMu0QfFtYQ0EuTTpnMfaLq4uLCdeLNzY03hlYWGkBzJpNx9hr6E4C7WCz6dTniN5PJeDBB8M5ncVoGzqqZJYI7DYQU2GAOlBYOiAvDR/WENv7mWRXkXfdICxLCICOW2QxtH+tLCQVfrIGWPcznyxNByNDir+zu7nrJDL6wXgcdDKuD0o1KpWKTycRPy1IqvwKk6D1YqehE9I6WlCtd/+7uzsG1fr9vJycnDtSEwX1aMBFj9q5j6DPG1jcMaNTnurm5sdPTU9d12qg1tOvKXAJoGY/H1uv1rN1ue+lTPp+3Tqdje3t73sAXtikBWrfbtV6v5/4tjEP8Tp5HGfn6TOFz4hd3u11rNpsuR/Q7AQjFh3737p0fLhArF0v7HE0srNu/CT87Fm8ouMIIAVddX5LA+J7Y3WKx6GWD9Xrd9z0yFzLX1M+jnK1Wq9nV1ZX1+30rlUpeFYJu3NzctGKxaN9//72zUulHBWDGvcJUA0xnn5ycnNjHjx/9tK6wj1tM5nQewzUNfaN1r6HuU/VN02Q/vN+H7kcBkNvbW0/s04NQ4zLm8vb21str6dc0n8/t66+/NrPliVvIy7Nnzzz+J1ZHD2tMuVgsXEbZP7Cp2KdaSozffHp6am/evPH2AMhgGos4XNfPjfsfXfqEYIVG/OzszBU+R9DxUCi3169fW61Ws/l87tkXVSy8fjQaeTBIOQylFBgdwJtMJuPgAOUUmjmmP4XSodRx0rII7bfBgpOZZRIx3mbJnj0YTLMlck4Na7vdtm636wyicDHUUVBW0FOMEJQJQQvmmHnVjYQToHTfUAGSLVKnZjKZOE0zk8l4UHF5eWm9Xs8DrGq16p9rZl4zStan0+lYu932wJo1RGnqCUFm9wBitVr1wIMjGrXUJHx2/a7zFAM8YgFFKHhPsZZpYEva38L/r1IE+mysI9Q+nHD6qLCeWqeJsxo6vciednmfTCYudyhiSjVoZMxJIUpL1IxmzFigpGFckEVk/zK0cVw4fzEHdZ0jBPWYTw3UCdYIchRAJuOD82ZmnpHAmHH9kAXINTTDN5vNEowXpRUr8yaXyyXYgQARvV7P+v2+1+WqvlA6KtngcB4eO2fsTQAZrQHWch6eV5l/n/t5Dw1dAwJqBaFCXak6F+rz3d2d2yAt3TRbHvlMCQxMGvrQUBKDTsWR1RIZAno9VQg7trm56U5quVx23QtggLwjbzjLR0dHNhqNbHd316bTqfV6PctkMvb8+XP/rFqtZvv7+9ZsNhOAfJrzskqn/ZJD1yjtf/qlPeZg71Fy++zZM6tUKtZqtZwNTF8Egntkj2spU4N9rMecDodDbyQMswX9iF2FIcGx0QAHrVbL/aJarZbo34ZPp+BxmMAIB0AVekLBWDLLWj6AbuF6q0pZf866hcFMeN9pNjstqA0DSmQDxsX29rYnhpgLTS6p3AMkw27T3mJhJp/PYz21rJPgAmYH+0afC7+3UCj4qXkwvhqNhoP8fAZ7KI2BEQaFvIb7WecIwY2YTxYDEM2WpbjoLrUHzHM+n/esPfuV152dnSX64/CMCk5hA+fzuZc4UF6hrArkWnUo9/rQHuRzLi4uHDDKZDKelCHpoiUXBKOhX7QK9A791KcYq3zlcISAQDgnupb088pms15OPZlMEkkRTeDTrwiQhfiO62Cf2FuUGB8eHjpzBt9Hk0HcozLR1C+BMUtvGvZLWv+lWKwRm9OYb7pu0DS0g2m2IGYvY3IbDvY5p8xqv0b8KTNzn1jjkYuLC1sslqWMlNPDqNPYHLuKziSxoFgB66H6WMviFLS6vb31JOj79+/t+PjYy8tj8X3a3MYAuYfGZwM14cWvrq689m88HvumV0UHHdrM3IkAeWIiqIHO5/NOadessZkl6PksBAuutPvwiDReq0E+NGUNHjQDoc4YKJ4qwZBqpQYdJ7bT6fiJJGoQwzkODeK6ledDwhMKXOiY4pBpwyWeOURbtRQp7GzOelHCxmkzr169StQ88xqdy16v5+tHMzztlcH6g5CWy2XfCwQaCLvuOzUMMeUUzhPfY8osfM861/EhkOUxIy0Q0b8zHzhygGnMNU4MSlQzUziuKvtmS8WsShKDGgI1ZPKhYwPUANwR9IT3j5KlH0q1WnWKs5ZIYkzTavI/Zz5/zkhbx5Ayy37S8kINnBV8IWMwn88/acSt1zdb9lVhPQgGcJLVSCm9G90G2Ebg1ev1vKxT9bTqDeyBBiM/Z87QsQQo2JQ0/abOzFMwo8JyBN33Cq6xXvP5PFGnrqUTCmphozi5ENbL1taWB35my8bzl5eXHoBQZsqpZtqsFgcHpg4AH/ZYy9awu+huyvAUZL24uPDyHLLJe3t7VqvVrFar+Ulgj2HFrfrfYx3+Lx0xsEb/xrryTAqqAhDXajU/XaZarbq/cnNzkwi2sYcq49g3bKyC4AQXyDf3mMvlEmWIlD4BzGALsY2wNlhjDcCZ4xizMLRv6jeRxEL/EMjrIQsESE/BrgmBBP6m/9ehr0kDJ1QfE3hjnwqFgp2fn7tcA0rxvOg/ZB77p+WmqiP19cyRJhPQ9VoCha3VNcOvhakDUAMgC3jL/ghZiKvmV7/wwdY5VGeG/oXeQ3hfMGoUqIklRNmHynxT5oP2tFD50oAPn0H7ssF209fp+iHjD+kvngWdfnZ25v4M1+r1eu4baVJcfZmHAJrwNevUq7qHVbZi9/YYUJBrMC8qR/l83hqNhoOSBOLoIvThdDp1lhU6mLUPmY/aHwyGzmw2SzDyzZZ9OrlHJRwQi3DKL73esPkPgTSrAJvw558T8H/OCO1hDEhLe59+jw31XTSGA3hBn+lrNaHFesHiLxaLiRIotWHoZRhPMZBa36frq3EtAGqn07GPHz/6ARpqR2Mx5EN+zmP84c8ufcJB5TuAR2jc2bSj0cjevn1rZvd0Qpw9LXdhQsj+0eAMehn0NrK/vF6VozoETOp0OvWjf8k2I/Qo6xg1m2dT5zmkQ6kzpYt/enrqJ6W8e/cuIaDMzWMU1jqHOluh0gyVqzonGDidC+aPrJ46mNpbhJIzhAmKMNklFNnZ2ZkdHR15zwl6HREIQDFV1NtsGUTSEI+GhtrQmRr5QqFgp6enDhKGjnZsqLGJKUidw1AxKSj1S4zHBjLhfa4ypAqqAJqZmStE5g+HnSaTGFDWSynhdGk3WzapRldo53czc2d/d3fXZrOZ7e7uuiOl64askjH71a9+Zc+ePbPvv//evv76awcAeZbRaGTHx8euZGOGUyme65ZF7hkHhKEZOT6TYPnq6sr7hbx48cLq9bqXl5mZlz0R9LGOzCW6Fdnp9Xr24cMH11mtVsuzvICao9EowXw0s4Re4Pf5fP4J2wrdqIwWMr0AZ6vmhfdy73xWCKpqRkl/1kwMNmad64jzd3t76324APe5d9W5ZvdreX5+bm/evLG//OUv9u7dOxsOh5+c/MB7YHc+f/7cfvjhB2fVEMhhW2kUDFW+Vqt5s+W9vT27vr7+JLhHbmNlyAoaxOQCnTYajVy+ze5PjiqVSl4mvL+/7w2FVc5+DlCn9/6U4yHHSm0k+57SUAblv4AkABSAiugqgFWOay0UCs68NTN/r5YpovuePXvmBy1Uq1X3TbQRo9lSh5NoKhQK1mq1vEE0/WTMkgw83bfIEvuFddSThRR4AXxEF3EfZP41EFvHCPdp+HPM5ulrYkG99u9RP3OxuC8J3tnZscvLSy9fVGBHk0mwwXVOtPwW8JK1KZfLls1m3T++ublxlg6fo6xgPgtGD30bzMwmk4n7zthsbO3p6akdHR1Zu912hk84B/oVMqzWvYbhUJYCe1GZiZnM8lhyZbGEe1iHJhuxSbPZzOON0Wjkc8HzchIVwLhm3LGT+J3ZbNZ7ZujahGV0q/QY/wd8AYSCPUvpGjpD2Rkh+Bj6hOFrlAG37pH2nGmxjwKU/B6z+7rew+HQjo+PLZvNWqvVskaj4QxqevYogIJ/CTOb/mqVSsVLzZE5Yk3ixL29Pfv3f/93T0oNh0Nv60AfG/wieh29ffvWXr9+bb1ez8vTwh5SjwFoQn0Wrqnu+XUOlXP9/pBO1Vhy1bXxJc7Pz63f73tCATBZ7SY2iF5emsCt1+uOQ/D5Gxsb1mq1/JAS4kpNrEHk0MSv9prVhCbsqNevX9tf//pX6/f7fsroQywaXV98VJ0jjYNWreOjgBpuBMVDTXSlUrHf//73znz54x//aJ1Ox+v8KD96+fKlPX/+3KlH2i8BJ1WBHkABJp3PxQDyWurylYqOAO3v71u9XvdAbWNjw5swmt0fIczRtPRWUOolm0M3XZip5r7ZdJeXl95Y6Pj4ONFTRecxtpghCrjuEXNCQ4EzWz4jm5lmrPxdS2FCh157BVUqFafXa38a9oYed6nvYy3pJXR+fm6dTseGw6FlMskabNYW54X/6ZGZILBkP2KORuik8LdQ4YT/07/rGir1+ylHDE1fpezD53ko+MHIqbOqzf5g2GjfKAJW5kL3Dg7l7e39KTIEn8rkQI6haFNSAFvPzNxRMjMHaAuFgh0cHNgPP/xgrVbLvvrqKy/rIKg+OzuzTqfjGQ51CEOFqvK/7hFz2jQoUH0BYJnL5bw/CIaJowkJkGmiXq1Wne7b7/edvYGOymQy1uv1PBPRarVsa2vLSqVS4kQMMhDcH1koAtT5fG7Hx8d2enrqZauDwcBLY7TsSI3hqj416oybJZtHpu1XDSR4Ld8VNFnXgAl4c3Nj1WrVzJaAUthrhqDi/PzchsOhdTqdBF09vDeeXQMvSok49p4AfDgc2sXFhfX7fTNb2jhkNJ/PewYWkJO5B1CHRk9iA3ZU2L/JbBlAZTIZL63R01Wwx/l83o8zzWazDvaGrIeHZCsGij/10MA0FrDyGuaXPc58qmwDSDJnWs6mjBQy9FD68/m8rz2fC7Pm7u7OSqWS98zDUZ3NZnZ6euqfZ2auR9mj+Xzems2mlxZvbCybi2svMmVSoY+QfQLQxWLhoJL6EQpqsM80AI3ZrC8ZMSai2TIRQNCnzrGCU6wnrwmDat3/mtEH5FIQJtw7akcUDAnZLyHIwxc+EQEMiRDuhXvGP6bH22x236sMNs2vfvUr++qrr7xtAM/xUBPhUP64J4DYdY5YwMlnqn7XxGHI+kvzZykJA1TXnkEkcEJ9zJ4PWTJm5rKS1n9LfUDYwKtsnj4vQDdr9k//9E/ea+zk5MT+9re/2fHxsb158+aTxqVcI6YzdU8qgLTOID+2b2KviX03S9p6vrRPqSaA8Pmxg7TKgHXKe/T0pnK57GwZmKkA4LBqNEFtZolgHdAbf5PjnRkwPjh1bzAYeA+oNJAmJmfhfIUgSGxd1zlCe4deUj2qI81Pjvls4es0tlCGXyaTcT8mm806q1TlUOMTKie4Z5K8W1tbVq1WPY7FbqPH2CtmlmA2K5OGqo6jo6PESZir/MrYfITzhJ54jCx+FqMGA1Mqlfz469/97neWz+cdpNCsN6cilEol29nZccFTqniIosZuls0B2qW/67UQVJwYgBjuHfZPJpPxRQyNqtYMs2F4vyoSRdxggIAO9no9z0g/xNwIn/EpR0xw2DzqgKEUocizdjCV2MAK0jCHZAJRZJxdryVlINwK1AGS6TrP53NXeufn525gzZZKncCRoFWZGLlczgNW+kGkGfTYiCmgh7ICupfSmAO/xAgVf9ozh/+PvU/3Yzy5AAAgAElEQVSzVVp+OBqNfK+oU2OWBPz4GceFBoqUX/B6HBVYWIzt7W2r1Wp2fn5uZuZBH9lEMiO/+tWv7OXLl1av1x2k1VK86XRqg8HAy0HCbBTPrcr6KcDT2FrEAgMMEQ7leDy2crnsACpUXphtBGZ8QQvVDDf6aDgc2t7enp2fn9tisfBrAoyFJzSxD5TeO5vNrNvtOqNtc3PTGx5Sishno9dpWrzKkYs5c+GcpTmEMXnUz1/HuLi4cFaZHreu2XUN2AAJx+OxDQYDBzZiWSoNSLRJJUdYsh9wKmE/wcA5OzvzrBRBZVqpn5bUscfUIY3pSrV7sEcmk4nt7e05EATTo1Kp2Pn5uQM6YTAd062xdQz/9hQj1N9pQTcDW4N/obZQ1zJM0pBg4HqAMFDA5/O5v6ZQKCSSIjiHZBMJPgla+RwFSkh68D98NwVb6A9IEkaz0epAsmeVvRaTVT5fP0N9jHXaxRCo0blOG+E+C9+X9n7VgdfX1+5jhKVLXDcEGjR4x3bR3FufR4Np2Mga8KPvdZ2Ru0KhYIvFPeOJMryDgwMH/WhezX4LgRq999gX5ZXlcvkzV2r1iMl+OI9mS7lTxlPadXh/Lpfz/lDMPfua/R6CrAo6qiwoa1zbN6isIMP4Sto7Y9XgOfFpms2mvXjxwgqFgpfxj8dju7y8tI8fP3rShT0c23/693BPPiQnnztiwEPsNWl/433oRdZNAWL8S5KwFxcX3swchgvNvnl29k6pVLJqteqnzJotmcz4nXyu+r/YPLPkkfAkywj0Ybqen597awetPIj52LomaYBHuLb696cYunf4PdxH/J6mO/S+00ZMnhTQ0nJ5ZQXDejNblpWHcqutSiBvcD0lA7AuYSmpskEnk4kfzoF/tMqfTPNJY7/r2q8aK4GacENpDV8ud99dm6Nbp9OpffjwwYbDoaOUl5eXNh6PbTqdWrFYNDOz4XDozV3DRmY4oVA/oZjyM4Kki8R79KEnk4krZrPlMc841ovFwkEaRVGZMO3XgBG+vb31oDN0cK+vr200GtnR0ZEdHR3ZYDBIZCseI1DqJKxbANOurcLGd92AZEZx9CaTiQ2Hw0+Mk9kSxAMgwwGBuqZU0W63mwgI+v2+B+/qtPB5o9HIjSJ75vz83LLZrP3444/WbrdtOp1arVbzeyEgxNkJHerYHIf7PTZPsbkNBZGM1TrXMXY/afce/i3mgMYcbL6HgaaekGS2POFJM+/0lFKqNvKDsmUtAHyYU6X6cn2zJasLWaMP1mQycX1UKpXs66+/tnq9bs+ePbN6ve5Aw2Kx8GDy48eP9vHjR/vpp5/csIeBhgYgyO66aaX6mTF6b7hnMExQaunPZGZezoR+JHOuJ7AAmMAA4XNhT0D5VoAG4C1G68QZ4RjT09NTd/4VzFM2gdmy/xdNcZWtw/zzXddC/7fqZ50vNbhhsLmOcX5+7vt5Mpm4s6F6A9AZ+Tk5ObHXr18nbKSChSGNXf+mgS8Akdm9LT09PbXj42NnXTUaDWs2m4kAjv4po9HI74+jailJvbu7PzJYkws6wrnGQSVBU6vV7Pj42H7zm9+4A6tOMGsRrlXs7+FnhnrsKUeoZ3UvqUOlzqGW9+IEsu4AvjA70TsKnjKPgMh8LpR81hibOZvNrNVqWbPZTDBjOAJWWVGz2cx6vZ6X4XBKFfeqbBpKPGJsL3SiWbIELhbM8xr+j58GYLPOoXIdAp8xvydc3xhoo981gCKxCOBtZh5MYo94XrOl/YLJjd7lpErWQBk2mUzG7Sh7hSBGe7spAEsvEzPzIB+gj3IQbMDx8bGXXk6n04SsK0CJzPLcGkSH876Ooac+6ZooM4q1wB9IO0VV1xo9rX7pxsaG73F6rqkuDt/PZ+v1iAPOzs7c7nE6lPqdJCkfYtSozh+Px/bjjz/adDo1M/N2Dr1ez/72t7/Z6enpJ41pQ7sY053q3yiT4anGKn/TzBJzqkN1rupI5oe4Av2Yz+e95E8Dcp4XUHs+n1u1WrXDw0Mrl8sO2hBPVKtVu7y8tEKh4Iz8bPa+B+B3333npzbRE47rw0ydTCZ2fHzscWCYrA/n4SFQI4zVwjlTYHCdQ31fZXnH9Go4VE5XvZZrwlaiQTsJv0ql4utI2wR8SBJVyJkSOLgHlTm+x/xA9gnlVDw/+vLk5MR+/PFHOzo6sk6nk4iDYmNVXBUDubCND8nio5sJm5nf5IcPHxyRonyFUyG05pVs0e3t/RGOZuaBg24Cs2XH7IuLC6dsQ9tFiYeNfjVbr9kFrVXUDQeyqYpXFxaWjZZ3qOJXR42O1d1u196/f2+dTsfevn3r7I9VII3+TRcdZ/+pgJpVikDXQoMDsro4/MfHx4mNz2BOtaETm4/ngtmgRob1gn1DxgmgjbpcqG9QQAeDgd+PXg96MI6LGrJVxnLV3x8CaGLr+RSAm95T7Ht4L7G/pQW64TPwpSyK6XTqDAKCAw0gAXgAMtW4mi2Du8VikWhMjdN5e3ubqEMFHIXuD6sK5hxMm4ODA98/BEaAFePx2I6Pj+3du3d2enpqg8HgEwcvBKZCB32dIzb/OPd8tjL5dI/BYkAP4wzW63WnxjO36GdtLqzynclknEWjJ4kAxuOsaJ8ljMpoNHLAhvm8u7s//Y6m0GRt+UzKa5jbWIYx3I+xADBtHtVWcX2AQp3PdQyAMnoHaE8Cnk9Pf8G2VCoVe/nypeXzeQccsZ9anqT7ThvtUWrIM+7s7CROLyS45z0bGxt+zUwm4wEcJ1to3Tb2MS3zGwue1Gbkcjkbj8duM3Z3d70vHCcWhf14dJ0fM9YtizpUNvgsBWjU6WPua7WatVote/HihctfuVy2SqXibBfYUwSCACKANZS+sI8oDc5kMr5HAF0IGpSxqs0xAWno/0bjfI6EXSwWDqjmcjnv34esqp7Gb1LHUoN39YViYBav0fnV4Gmd6xaCsaFDHtrqNBuov6uchX4R/aFg1FDuxz5BT1PyQInF5uamg1Xb29tWLpetXq971h3ATE+yLJfLzkzHJ4XVrL4sySns4sbGhr+XEmCCSUoyYiw7htoj1cWUEVUqlTWt4HKNsO0cJb9YLNzvCwMcs+Vei90rAzuID2tmHoRpr5e0QJJ51gBNdTVxC0kReuqxL+iFAosXwDb2/HzH32KvsL84Ve7y8tI6nc4nz28WB2liP+v+XtcI7fUqEMIs2adQ50R1LfGeJtQ1wOWEpcFg4P8LT6/kq1qtWrlctt3dXU/gIbfoPmUds/dubm4SB8MsFgvb2dmx2WyWOKX05OTE3r5968wLZSaG8xObr9h8Mn/8rj4GzY6fwkcNY8ZQr4f69bFD97lWpCizBvB0d3fXX4t/yZprvBGCMACwrClxgZn56Xv0VDRLEj6wowAz/+///T979+5d4qCkNIAq9nPs+UN/la9V7/usHjU4pFCtNzY2EidFhAg9GzCkKuk19TvX0eOzoCSp4HENDUD0YfW4Q1W2KEn9O8obOrGZJYIbRRKZyPl8njgi7+PHj9btdu309NSPG0tb0LT5xdF/quCezwkFMHRQ+Zn7RyHoSVpK9WWwH7S0DWVnZs6Ugg5IfW9MeZmZMwXIPGs2jx4R9O1gjxQKhU+OwOQZVylEHQ8FhXq92Pzy/XPW/zEjFsTG7i12P6uup0pD36PlTiiysLksSluzSGGWRNcgBM0YNJ8mWwKjBlT87u7O+x5ppopgCRYNYAMKn+O+6aPS6/U+cczSnIuH5nZdI7aO6J5wzcMGz6q7dCgtXt8f7gfWgedmLQFr0LnhvGhDcD3tSUtisAvaKwpdoJ+fBiLH5icMGvTnMMDWEgKef12Dvlo6z7EsC/dCTfT29rZVKhW7uLjw18BM1dIWdaDZy8xfNpt1WdE1U+DTbNm3S2VOG3sry1DnKC3LyQgBFpwhTiKBzUp/OkpnAVxXZQDTAGV+f0rbmHYfIbUamYHFQCBMtp4jY2GnsedZRz2yl+BLe93Qp8bM/DQasriZzJK1oeAR/g22D0d3Pr9v9kwjS+wx8w+4B/CkPQFUH6qDrjLHuqz60rlMA2jXsV6hvY39/Dmfm2ZfAULxMbAl7AvKs5XOzxe+EQApJY0EhNyrXkuP/tbeKgp6ZTLLQx/wj+lXxboCAA2Hw09Kf1c9byiTfBblz+saqrex+/ggjLAMaZXt1nnBnukpMqwh/SZiLEL9LA2I9fX4RSQprq6uXN8BEqEbAB1W6TE+S8Ef4pY0YC2URf1b2s9PoUvTZD/Nr9L5DZlBfA+fWf1TZJGyYnxQ+tWw3uwDWmLs7u464KJgpyaJAcB1jfkyMwe26UVDRQU9ELW8OZyj2JyE/48NBWmeyrdhpO2zcF1jAGfa/YTrqEkujTOYb/4fXpvXaEkh98J7tBcucYWZJXxTBXmwzYCgb968saOjI3v9+rUNh8NUpvGq8VDcGMNA0sajS59CxcikcuwZExNzthTxDf+v18VgZbNZdzhwVFSRZzLLYx75roaLIGY+n9ve3p7fr3aKpzSCJkRKe9KyDAafwSYgAPyf//kf+8c//uE13gAQnys8sQVc11DDFiJ5OlAqzBXPghIjM4zToOtH5qBWqznV9/b2/tx5ejT8+OOP1u12rd1ue+Ywm83ahw8fHNFkLTY3N63T6diPP/5oV1dXvv44yTgmrEcmk3GHFoFTpaZC/ND8qkD9HCePz9LSji8doSMXM8gx2XssWBO+VudKQTsz80xwtVpNrD8Z/mKx6HuGksfZbPbJKRhKZU5r6oUct1otKxQKHrCyDzG87AUYJ9Pp1N68eWMfPnyw//3f/7Vut5tg84VzoHMYGoh1jjQZDNdXX488AgJz2s/e3p43RDMzK5fLtrW1ZR8/fvRSm5OTE2dCIct8Dk0xOUaZ0lIcFvqc3N3dJcAx7flDAEjQDviNk8pRzsp8VAdN5z1tT2ug/Ji/8x0ZXDc9WANXDcQUiFLqtNpLZbyQNcWRAGghcAZMgYlByR8BF8xC2DMEdXxR8kQZMmUXuVzOs/QEjdqE/bG6jjmGMfuXv/zFzs7OvIl8rVZzJtUqm5gWVIf6+ymH+jYaAKtcarBERpbTQuiHl80uT+HBXs7ncz/RSW0TgVuj0fBSYdgss9nMJpOJJ4I6nY6D1LAZ5vO5s2LQe+wr9pmeqpHJZJzNo84x+xAnNrSRMWAmtJEqi0p914y2JoDWNfR6qgtizm+oW3k/z6XPy/s0e4xDTzIR+abpL+VvAG/4KDs7O7a/v+97AZ+lXq+7v4Qdw3ctlf6/9s6st60su/eL1CxxlCzLllWDU11BA51+SJ4CBMhDPn2AJEB3urqTGlx22RopUSRFarRI3gfjt/U/S3sfUvJRdd97uQCCh4dn2NOah10LxePBU+VL9HU8HmeiDlBOdTv08/Nz293dtTdv3ti///u/h4LmHtemwQEzC3WxioRYtDPrWufK0/XYnAKlUimkgD179iwUFGUeSSdGh/G8RJ0YyL/QZiKnBoOBmX0yVB8eHoZ7SUtEJma3W6/seoCeIjezFobDobVaLTs5OQk6ka59xbm88VDc5/jXBO8USDk4zO4iI7StitfqJKpUKkGGWV9fD8X4dbOS9fV1azaboV4TPI9xGQ6HmdQpUhoXFhZse3s78CIiF29vb213d9fev38fdtKkwH9MF47J2z6iKaXMm1kmsID/ipZteI83oPE+T2NjvNtD7DzncOKReaNlEBgfovjMPslV7N6FQVfrQCk+AOgXZtldLdU41O12rdPpWKfTsR9//NF++OEHOz4+tk6nc89gPC3EZHl/HBvnGEyMqFFijgD/+vXrEHr7xRdfWKvVsuPjY/v5559DeDMIpEQfZODYNw4FHeszlnD9YEgBERFIYZr8JifV564xYRRchLnxH55Ks7t0KkKXEbKOj4/tT3/6k+3v79v3338ftll9rJEGeIgxoMhnxxAO5WBvb8/MLISOMj6E3Znd7Saxublpr1+/tlevXgUvwmg0yuxoAdHU4tKbm5v27NkzW19fD1tx39zcBKZkdhe+psYCVQ5Ho5ENBgM7PT0NOY2+DkDRyncMETEmqZe0CFDhOM94xDkv+CiRndZQBZFUL4MaRDHQlkqlTGFCvrUNPIv8e80X5314oXRnBohvpVIJa240GoXURgwKZhYiq/BI7e3thUrthELqGvDKhwqpGk1SJCjDVvBhvZ6Qe6Hq7OwsKI2kxSAcUH8GoUHnBlxRhePi4iJEamDIQsHgHRQ33NnZsYODg3sFEpk/PkRTkF/PNdTL8OmTKeOMVwpj/8dwUOex6DnU+kYoXXNzc/eK9mKg6XQ69t///d/2xz/+0f785z+HlCeUEOYF+rawsGDNZtNevHhhX331lT1//tyq1WrYWhSeRD0KM7OdnR17+fKlra+vZ/CHucawhjPk8vIy0MelpaVQqPjFixe2t7dnJycn9yKgUqDvYdtKs080SEOMmROdT13TeUoG9z8laHQS61hp3tLSUmgjdRGazWZQis0spBEig2CURm5hnaCkr6ys2MuXL61arYb6CdAwxoddKZeXl61Wq9n29rZtbW3Z1taW1Wq14OHXwt3gfLlctsFgEGgt6wsjI8YdFZDzUpliOOfnR9e/8gSeV2S6hTphvEEvRie0bbHz/j5/zP8+gg/ehmOQMcaAdn19bbVaLbNOeB701+xTgf7T09OwKcXc3FyoMcPcko6qsitrlDFQOVbTCeAbMU+/fvScevLN7tKJigQiI0iT1Hapocwrv5zXtutv5A2tWQF+qOPGz39MTlIjkUY64iBqt9vBObG+vh5oBumKRMPlga6ntbU1e/nypTWbzeA4hXfkKYDeIKDnVa4r2lATi/CIzRftUJpllt1pFsdSqVQKfFXrPir/IDqCdELo6draWhir+fl529jYCHKkzpvqiBqZRvvVQMrYsYba7bYdHx+HTRXIFoiNewy3lM7qPSk65ee0aHqqbVP5W3kA4861WkfKG5Lz3gFojTf0fq09Y3YXJU46NdGo3mZg9mksSXEbjUahntBwOAy8mfYTnX50dBRKmPz44492fHwc6sw+Fkfy8JBjxcXPMtToQkLJZpeVWq1mL1++DAOoA6CLTRd9yjqngjVEFSGHYx/eref1P4gjdUpQmDEcmN2lN6kn0ytHHNM/LKkHBwf2/v1729vbC9vffs6E+rF+CogJWCmCCnG6ubmxTqcTFjzMGY8hguDKyoptbGzY5uambWxsWKPRCIiD1RumCHFmvrwRQLdKJOTMzILHUj3V43F2i1Dywtm9Qq9/yigJD8oQi35uTHjw68YLbmZ36Wwa5ZMiDJ6ZxHBRvU0qzCkeM/4QcObYzO4ZflTJh/jqPOMtgXmjEKklnTkm+gAPFJEoynBi4IWKlELyOaBjGxt/T9RVSNX/8cqTfsoHw+hgMAiRNGpA9mOlKRn6HozhjBvbWj579iyTD+774KPxeBbMVOc8NQa04zFjP4k5FgFK69WIpjvZsV7H47Gdnp7aL7/8Yj/88IPt7u5mQuc1hYAxwUOvtbtQ7DT9jSjP1dXVsIUvCoEKzXiPEfLNzHq9Xngf9+gOfcpnJ4017yFyh3WpEVme1z/UmfGYex76fMZDDe1a72B5eTnQT9KBNYUM/OKjdAzlg+gynttoNOzFixfWaDSC1585Yy54j0asEoHB7ohmd1GQyCu3t58KzQ4GAxsOh/dSAVRhJarG80jPC2Lz4XFLcTdmRCmSL8bwPdbGGDBfjwEfzaB8y+yuSKXZXarN9fV15rxXslRx5rzW6fCKna5TfY439FOWoN/vZ+onxsaL75Ri+VSg6UgxGUX1Bb/uYvIPeIwugPJH5K3uFuoNNV6uUVmHtmhNC2RQUhW1hhyKJx9VgvOAd+vOYJNSp1JzlGcgeIo51T6mnq9jq2l+GFuoCWJ25zDn2xv6oVs4mKjhhXOXtUANUtJwPagRWx1J+j5kMnb57Xa7oTi434Y7hjt55/x5YJr1/muAn9NpebLeF7teHaOqt6ieoQYjjYjxOMrzkIHG4zuHsTrD4H8EYXQ6HTs8PLSDgwNrtVohXfhzdHrvKIiNy7R0derUJx20drttrVbLzMzevn1rvV7PBoOBtdvtTGg74MO1YlZWDbXUgUfJR+hT7zCDjlfdd5pcXkDz9m9vb4NnV/P9PSHl2vPz8xA59NNPP9mHDx8yRprHCJExQWeSZe0xkIckMeEZRjQej+3o6MiGw6E1Gg374osvgpdQdzqo1+v21VdfhUKKRD70+307OjoKBZcPDg7Cc2Ga8/Pz1u/3bTwe29nZmX3//fchKqvdbgdh89WrV9ZsNkOhORj7jz/+mClGRe0MFBFV+LW2ziRG4tfotHMCQcgr1PcYiBE7L7hAlFAENJUhVsTb9z0mNGoUCsIonvytra0wts1mM4R/N5vNYPEm2sXsrk7DaDQKAgzX6Raumk8OjqsHr1QqZYRX6EK/37dffvnF9vb27H/+53/CDk8+3YlneLzTMYWOFW1s8+Prz3klx9MiBAFoFYXXKcJeKpXs+PjYWq1W2Aqae7S/4J+uHzWCUTCSgrS//e1v7fXr17a1tWUfPnywcrl8z/h1eXlpJycnNhqN7D/+4z/s22+/ta2tLVtcXAxFxIk+zAtb90JxnlLINXpMSDWKa9FGWgTK1dXVjMGJrarxkA+HQ1tdXbW3b9/aTz/9ZG/fvrXj4+NMNJIqboq7WteFnG2K15+engZnQblcDgq7ptxgPBiNRmFHoZ9//tkGg0FIZXv16pW9ePHCtra2wpj7SAEd35Swr+fANQxD4/E4s/W0Ps/PW0wuSP1fFHihE96H0A8NQI7Q1Gki0q6vr4MhDQUQOsl6ZE7G43EoZriwsBDmAjqJ4eTs7Mzev38fdhAZj8eBBlLk++zsLNRlY6cnjLSqPO7u7mYEV1JFqeN1fn5uh4eHIT1LBVSv9MfmI4WjnNe1xfgWBbpeNWQ/xSM931caHOMHfo2rwRsa4w1z4/E40AgzC0XvtcA4cuvCwoJtbW1lUhUpMMz8Imsh41CoG3qPAcCXA4Avdrtd+/nnn0NRzNT4eyUsJpfiTDs9Pf28iXNQKpVChJemO/lagzE6wFzwHLO73bhIw8R5CK0dDAZBwcaB6J+JA9h791kH4D76A0X0wT3mp1KpZHatjfEiT29JSz46OgrRwNTY0+2BFb8mybJK41TZLQpieOf7p+1UJ3u9Xg/pvpVKJaSUal1E7xxBhhqPx7a3t2dzc3O2sbFh5+fnIV2QAvyqb6hx049brVa792wM9uxIhq57cnJi79+/Dztf6iYVSvt0DPLkO52f2NiqPMoaLHoOzSxjEPO8OnbO98f3NwaMDZGdWqcSngO9WV1dDWsWmZcoVWhFuVy+V+YEuReaq7oPToper2etVst++eUX+/DhQzDSfI7+FpNr/bM8Lk6Sb6be9YnOMQA0hh159HzeYqOR3iDiO6eTFvMUYbGmkJcyTA37Ho/vdpjRwm9qYScNh/fQl/Pzczs5ObF2u20//vijvXnzJoS6eSPN5wiROmHTWNceCh55eCe/Y+2nPRcXFyEcFSFhYWHB1tfXQ/RTs9kMyjX3IEiSm/vx40er1WrBU6/KOilqPA/kRdmYm5uzL7/8MhjWtMgaggmfXq9nw+HQ6vW6jcfje89eXFzMEIPYXKSQLEZ4lNjmEdqiIEVAARgF44fCgMLnw8NTQhC4rilD+k68Xwg5Osb9ft8WFhbs48ePYVu90eiuTgPGGJQfny4JqJCm4ZU6D+SXnp2d2d7eXti+8v379yHVI48Wcaxzp0aEovFxWoNpTDlSwKOnhdcREMEFM8vQQhijep1IH9XwS4w1z58/t1qtZjc3N1ar1axc/lTgm93tVKGDXrAz19HRkW1sbIRIAMLlEZq0RhH95lvHPUa79LrYmOkzniL1SZ0ReGc5h6BONKcqcH4XLu4tlUphraNAI6SQnjse3xW8431EjW5ubgYjdrlczgg9JycndnR0ZIeHh9ZqtYKQf3Z2ZhsbG0HJQIglVUBz92Pz44VcXVt8QzP4D/Bh0zqHfp51vp8ClN4pqDdOaxYQCdNsNu2rr76yly9f2osXL8zsLnKXyAmiK3g2uPD8+fMw3s1mM+M11NTDer1uOzs7trS0ZM+ePbNGo2GNRiMoN7ppQqlUCtutE9nMennz5k3ol0ZhzM/PByMatFKNwDGAjsQUDL8eOO89pEUbv6FvOocphcLzfd+Xad5zc3MTUkuRH8FtFEM+GB5ubm5sbW0tPAvvPhFR4/Fdij3FSY+Pj21zc9Pq9XpmLWm6jo9sRX7FYIjBlGiPvH4q79Nx0ucTfcVW7kUB/Sfij3Wi6S7IEX5elYZAy7Q+DLXbuJ/6l6QFs3b9+lHDn+oi8FuuU/ld0+yRWZC7UkWSFWg/RiZoDIX5b25uQgHbmMFtkmHAy3lF0tUU/ut8Qdu4Dt4IPTKzEAGoES44DXV9qpLb7/et0+lYu90O+Mb6552NRiPMj8p6GMnH43F4N3OJrPvmzZtQs+To6ChEax8eHobIZZ9Wn2dAixk1Ysep5yFHPYUzMUXboeWeB8TWWKyPXs5WnAcXFb/VwAI/VscbjnnwDFuA2d1OsswhNoJerxecGt1u1968eRP0BkptqK6T4g2pMdK+xsYAUBnI62QxmHrXJ0+MAAZXiZpvrP+dWlg6Ad76pd599RjwAbzCpe1XpPGeTP0PAfr8/DwUv/3555/t7du3oWgwlt6iFPOnVPBjSt80wgnKgebqvXjxIoTma0g23iNSj9jCst1u2/n5eSh4yRaDZpZR2MzuCjYzFig5CwsLVqvVAqNjLZhldycaj8chJ7XT6QTFEM+3Fp2aVjjz4+GPU8YbXX9PASlDDXNNpAoeCsZIt/j196nAAi5qcUnP5H1bOFaC6q/XKAcVrlXIis2P9lfp0MXFRaip8csvv9jPP9Ev3jAAACAASURBVP9s7XY7MOxYtFuMqPqPMoengM95Lv1HIL+4uLB+vx8UIqJoUt5ihVToPQWjMaoSMUUUgKZT8WzwkogAvPUIyWaW2XnPz7FvY0qhigmEvyboGlGjmPIjjNYqKEDvdC1zr8/zVscE/A3lAkWhVPrkXdIC72Z3BfOImGC7ZpwqpVIpzI1u321mGcNEamxjQqMab8wsQ6Mn0cHH0OKnAlVSoU+0D0UC50Kz2Qx44WvbYUTlXpQ15ouoG3iT2d2YodxjAK9Wq7a9vW31ej28C8Bgq1EIPEtTiKGDml5KzSPeGYumYkwmjZniI+DTnTTypEiI8SXPLx66xlJ8XeeIiAp1NFCvSnFf28H9c3NzdnFxEaJh4Hta+0SV/hi/5z5N0zC7S3MmTZIoK43EiPUxxSfhhSoT6M51RYCXx2PpRpr+QPs9oMAS9UuaII7a6+tr63Q6oSB/rIiwgtchlOYzv9AIb6hh/Fj3aqRPAX1SfFlbW7PRaBTkbJyZMQVxGsiT5YqGh+gZrGfFKb/e9Xm63lH2la+pjBKLYtK5Vb6l+iUZHUTpY6BFxqTMglfuHzInedfGjKWe5/7aMmpMdvNzktJL8nQFb0BTvU6dGTga1ODHc2gbz2MNoFeyeQaRNLu7u2HXZuiBGou07bFx8OOVMsqlxndaPXEqjukFZn14TBmLNUifoQOqi/vq6ipMilcOIYDcAwJhfSZyQhVxzeNFUIaIQlj9N0yIaJqffvrJ3r17Z//7v/8bomiUABSJJE+lcOQJMP5c7Fs99uR+4t1TL/Hl5aXt7++HEM2Tk5Mwp6VSyWq1WkjHYdet4XAYvHoIPiAonhGUEK2/oGvBez4oDsV8l0p3dR+WlpZClEiK6aeIjIe8+fLIXiTkPZeQ6u3tbXvx4kXYOpa0C4S1GJOH6EPovDCieI/xzOyTUk/oKgISc8x8m5mtr6+HSCj17FLRnfdrmL43IBHuyI4mu7u79l//9V+2u7trHz58CGH/kyLd/Hk1zChjL9pboWvVC4GxtaiMSJWOUqkUjKitVivgx9zcnH348MG63W4m0kOfz64SRMOdn5+HCEWN5iBce3l52f7+7//ems1mSEOE4cV4w3A4DNF0KCK0lSJtsdBSvx71d0zhUqO8PguDBfSAdVMU6Pxh+DSzTN0DdibDe0sILqmY8By/FjiHoXt3dzeE/h4dHQXDd7/fDzvE+FQV8JbdWShOqikxBwcHgXaz65BGxcaiKlKCor8WHGUe1OPN+MXGU397AXVageYhEDMEan+80cl7sLX+BQYX+sLubIoTZnfOCN3CF3mHWlr9ft9arVYIqR8MBtZsNjPKGx7D+fn5TDFLFBX4tTq+vMCrUXRKV/xc6XhADxUXVRCOecyRvZi/IlOfPMQUgTyFQ49Tir/er4o6Y6BKA3QUIEUS4xjRKBcXF+F6LTS7tLQUjH/Pnz8PRlg1BLCe+I385eVbdTa2Wi3rdDpRA35MGdR51rEcjUZhnRYJGFXq9XqQGVX51fak5DMU/ZWVFavX6/bll1/azs6O1et1m5ubC1FKP/74Y0gDvby8vOfM8bQA2UD1C3QFcMvMQqS3OnGRPVVOjim6im9E7bTbbfvLX/4S0uhS0ROeL8b+5x08v4hMgNR7/HcMz1j7jNfZ2VmuA0+NEjEcJzW40+nYhw8fQtS/6hvMgTqHvTGQHbZwAjLH5XI5RB4eHR3ZwcFB2IY7ZaSJyXGTIKb8az99KQ/lLUWB111i4+0h7/rUOHgeq+9HXgTvqAWHEVad/Fzjn4ehGri9vQ1p4ycnJ/bu3bugF/X7/QyffCx4mTVluPH0bJJ8M7VrQ4mB5mwT4aDeJ643s+iijf3W67UTo9GnUE/yvVmsKIGKfFjitVBwzAIH0+N5VA3/+PHTNqMfPnywo6Mj+/777+3HH38MtWl8FM3nEjm/aItWCoGU1XWa9quSjpEKrxFCyOLiYiiqtbu7G/L8+v2+mVkglOvr67a1tWU7OzvhOVjCUSqwrGNwIG8bBYdQXrZT0xxWnRNC2+bn521rayvUzxmNRvbDDz+EHXGUyHmGFoOYQWca5eVzwTP1mLBSLn+qdr65uWn/8i//YhsbG0H4AVfwOuCx0Ofr+Kn30+MZircqpdfX11apVKxer9vR0ZEtLi6GnWp4HsIlwjwCAx5E8NAzT9Yg3uNutxtSOf7yl7+EWjQooo8htn5MoXFFe39V2FBhRGkn5/z5mBCAgndychLSVc7OzkJUjZllBB/GsdfrBSPZ5uamDYfDUB0fAw27t1UqFXv9+rUtLi6G3UsocKtKODhL1Nzm5qa9ePHCnj17FoyrecKqB70mxeBj46RjjYJYJC5eX18HxUrTT1C8WX8IG51Ox8ws4w2FltEu5gr+RUHsw8PDkDJBYWyMKY1Gw54/f27b29uh2DrPZTvuVqsVwrQ1egYDANv0Ivg0m01rNBpWqVRCvaG8OVKawTzosedrKcFFBc6UzPBU/DEFKH7MbaVSsXL5Uw0h1nWz2bR6vZ4xTLBmUZoZQ90NilQIjHCsYyIVePeLFy+sXC7bt99+G4o982xoMOkDZ2dnAff7/X7m3WoU9pFCjC1t0DVMv5CjUBb9ThUqZKoBw68d+EpRoE455eVqCPZGUKUfeZEJyIk6Tjq3Oi6kcWuReyKveAcyKGMAnUW2QramPsfZ2Vkw1Gjqk99JTUHTajAmaJHbGC7TNo5ZewBr2iwrDxYJKOunp6ch+hqZnHZBc6GfCuDp0tKS7ezs2NbWlv32t7+1nZ2dYORotVrW7/eDsyBWu44+MsfgifJbNdCo3EQEI04STTWPKfL+nXyXSqXgADg4OLBXr15ZtVoN6ewaoZqClHEEuYZ1V+Q8qnNNcSamuOu46FjHohk04tDLhYrjfBqNhtXr9Yx+gh6htWkmjRtF+Pv9vh0eHtrx8XEohdHr9UJalaeh+hw/3572+HOx9eGNlQAGfsXVIsDjlxo/mDeNIPVtnaSreMDhQQS37uCkTh9N5wfAifF4nInWxhhOkAF084cffrB3797Z4eGhHR4ehp261ODq6b3vX97vmFHKz63XE2l/IYYafXmsMdMeAymrqxIT771BceIehEv1EvmPhpzrO1RgUe8EBRv39/dDoahOpxMspyrwFAUpwbRImIQwecjFsUYz+KgiDCOnp6dhvNSriqDKtuooCGYWvEyXl5fhuoWFhVBIj9BVlHpCeSn65I00WFMhsmdnZ0GhIo+fQmUxoSP124+LMo1J670ISL0bQImo1Wr2xRdfBM9co9Gw3d1d6/V6wcuv9V/8c3h+DI+0BoV6SRXHuNePiRo+PH5rH/Rjdidok1bT7XZtb28v4CiejcdUaVcD9K8JMYacZ4yInUO5RRgn1ULHIrYWYVrn5+fW6/Ws3++HuVUFjQK1MFGEHBR5igTTHl0rahQHd2NpaEVCStlKCcePBeq/aL4071cFfTAYWKlUCpGYZtkizr5QptkdXhCm3+/3Q+HObrcbUlxQSnBYIFwRaYZ38fT0NBgxVfFQz76GB8d2KEmNncedFN9IrWsPT8n/Hgoe93R+1FGEd0/XvtndxgWaBoFQhocd3ELARBBWhxMRGrwLAZZrdD1MEi69chNT1FLzo8q79pN26z0oD+C9RiYUDaoUxiJFU2sw73kxnhAbUzVIad0fH9GtyjH/jUaje/U3eJaPgkSe4VpNVVRlj2tZe6TfEQGjjhk/Hl7J9sZVHU/aWySoc0uVT28o8fOrbdTaLuvr67a+vh6cD+zsQs1ETen2YxIbW/quaaneaQsv1iiLx/AenfN+v2+9Xi+Mhe/zY/kp7SoSJ1M4FpOPFW9VF/NGrRTE+u3nBxyDT/oCwina59c7eg9OZYx8WpM19pzY+KRkOX/sadivoV98DkxaizF6DJ2BJ8LfmCcfvaj38dFC38ytGnII9ECWopTJ4eFhMAprHSzf3iLGxUPKmJX3zqkNNbrAFcEeI4DnCXAQWyxrCCic0/xf2qUCBLUBtN3e6zEajYKHgfzC4fBTEdx2u20fPnywd+/e2f7+vvV6vahC/5TK3VMrM95IFhO4dY61PWdnZzY3Nxc8+Dc3N7a8vGz7+/u2v79ve3t7GQMKc8NOUCgYpK5dXV1Zp9MJ3kCib2q1WlgDpMagXBLKj+fCMxuUoNPT05CigdcZY8Hy8nIoHPbY8c4TCGME/HMgxZiUwFMDqF6v2/Pnz63RaNjq6qpVq9WwrTIh9544xARejUaDmCJwsj2sKi0I5yiOKBgo7bVaLZPLTzt4Nh40n8uNYEihxZ9++sn++Mc/2v7+fiiQ+pB5jDE9f68q4EWCGnpjuOgFBY2G8zSH9sGMoIWxcG4fVafRHu12O5zX0O35+XlrNBp2c3NjL1++DB6Pb7/91o6Pj+3o6Ch4lczuvJpEdbRaLSuVSoEmQ0tj4xpT8POEFb1Wx2U8HmeUSJhwkXPJjj2Li4t2fn4ejMCkBbD7TqvVsuvra/vpp59CDrQaalQZwQEBv4I+DodDOzw8tNFoFAT2+fn5QMvMsilXw+HQ2u22dToda7Va9u7dOzs6OgqhvYzXxcWFnZ6e2traWij+DY0GdycZVFLCJTRE58h7G1XRnSTg6fVFgvYvZiAGSCEaDAYhDdfXiYEWahtR2ogeZVxRJs0sFF0nqhA5hPlDKWB3RYp/Y8ijWPTR0VHwFPr6XCnFR5Vg/fgoU4wNOo/D4TATbahGXlWIoOtq1C8SvHEhRiv9GtPzKWO2p8cY0dTravZp3RD1gqKhijyFxUm9IHJR23x5eRloN3OtUQD6TIwBCurQYM1hqD05ObGDg4NM/QWFSYYEb4iDRxdtqKnVara5uWnb29u2v78fCswOBoN7Ecqeb6rSVq1WbWNjw549e2YrKythPN69e2d/+tOf7OjoKBTi9fI864T1iuyjuo7WEPKKuhoImCN1cPm2TwIcKsfHx3Z9fW3Ly8vR2i3TgOelRdbX1Hf4j9l9OqtrP9YXT498lDXP8bgLvyf1qV6vh81PUoYTD6pPai2vlZUVG48/FRamLk3ePEySV2LXeFk0Jh+q8fCpUp9SsjRjrnw7pjsqxGQE7kGXUP1eHR/X19ch8vT29jZEa3ONpkSpzjAe36Ww4QShFAe7oRIRpUbxx0CMt/gxiP2vMpKWl0jB1IYaz6BSDfEwLYIogURgJBySXQzM7J4HSfMa8ULpNogU4yN9hnu0MCNbFvb7ffvuu+/su+++CwVKFSFjzL4IUEKfGp/PgZTFzhO52G8lFBquv7u7a8PhMES9vH//3k5OTuzy8vJedXYUc1W4O52O9Xq9kO6EAr+6uhq2BiZscX5+PoTqYxUlH98jmo4lDEmfzU40CHVsqzdNfmKMocSUSrxjRc6j97DF2nZ9fW2Hh4fBe04I6FdffWU//PBD+M9bkD1jNbtLwVCvPYIjOfPqEVRCPBgMzOyuIC04qVEIir94/Qj/Nvs0f6enpwFPW61WwEmUTy2Gmjc3eYzEX0/bNMyzSNCQ35ih2v9W76mZJQW+lNda58WfN7szEmh0IqHiMEGzT/Nxfn5+L/WxWq1mIgZQMlR50LBmjHmxiEkVuvXj506VMlWk9BrgKRTEdrsdFBV24WGuTk9PwxbdCPTdbjesbcLhGRvlYfQDQ5kaYsw+pXOurq6GdIpnz57Z8+fPbWNjw0qlUhAi9/f37eDgIBTKoz0q0GH0nJ+ft5OTk2D8MbMgQOHdjyk03lhjlhUiFXd8iLdXbvRaryDr84rmiwB4z7d65li7ula5jvnV/iPoExVQqVQyfULGqdfrQQFQY87FxYUtLS1ZtVq18/PzoKiTxoqBjlpujJ96FM3u11Lh/Tq2Oi/q7AJnwGltO4YblaUwUEE/NEVjOBwGZ4umthYJOnextemNhvwXUzJitNkDPKvX69na2pq1Wq1QD4MabPBHv+Min0ajEeggtaYw0NIfotq8HKU7zICnpVIp1FdETuKbXTtjRinFbT9OXqGExsWMmZ8LpVLJVlZWrNlsWqfTsWq1asvLy3Z8fBz+V97plVP0BmoowsNOTk6s0+nY27dvQ52uFE3zCrJGjoOfjKnKKtyDgebq6iqktcEb1PgTi+bxQB9vbm5CqlalUgnpazHjRYou83uSEeVzIaVf6LvUUeXrH2r7lU54I7+X05UeEymKYt5oNMLYp1LZfXQgeIYx/Pz83A4ODsKuT6ovaF8njb9eF7tGxyB1rM8g6OAp+aK+Dz6IsVZleMXN2P0pww8GNaK0SfPUKBnmRDelURnS1x8y+ySjsTva4eFhcGb8/PPPdnp6GnZ3yrNr+H485nzqGtXnUuOj8KBdnyY9cBKD4xqzLBLrsX+GEmSYnxJtJgchAEFJGUvMasYCw3CA8QCPFML151rcJo1FnnHk1wZPWD1AXEm1wFCC0KL7z3sBQIUM6qSwM4zfatvsTslHEIFhcr9Pv/LtVIJO2Bs1MhB+19bWonU8poUYs/N9LwpixpTYNRgyDw4OQrgmXnNSy3z7/bHZHXHWsEQNT4T5Kc4hvC0sLNxjLvpMJfy6LlBQSXE6ODgIu9UcHh7awcFB2B7RFw3Wd0w6jv2OjbUqaEVCbA5jjHwSeKVXhR59ln8m/2NE0Zx7Zb54jvr9fqiRcHNzY+/evbN2u52pkM+90Nurq6twH9vYMr++n6lv39dprvs14PLyMhTfRWHGA0Q0DYYa1nXM0KpGGgxySoOhTf1+PxhiMKAOh8MQBYMRlEK07XbbTk5O7Pj4OBhAPR9FqSCyBm+/7gyja2cafE7NR2o+Y/JCbI4n8aUiwMshZnc8wSs2MaONtlmfqWHY9JlCwCjX8DKNqvGpE6wVzqOU8Cw8/D4tL0bX+U4pQv5a7Q/rlGMV1LlHhehyuRyM/aoQFQWPlZeUZsboaJ7SSZ91lzfuUWO5jpFZNprAKxvaJo5RLJF1lVaAz1o82Ed7aB05r1Sm+H1MkY8pxU8BmjKr4xGbjxQP1ahOIuTxpKeiijyP13epjAKu+iL92iaMKx4HJ9FID0qD/A5bRYz/X0NG9YYiT1sVYoYJ/5+uTZ6HceXy8jIo9kQgqpwyqQ/QfnRE8Eh3ePtr6Gkxflu0jJqaRz/ese9YWznWZ0MLiYrRmrPeWKMZANA9nqORnAoY7qj1p2mPWtf0bwGm0RmnjqiZ1uI0jfFBDTJ6XsM7UQSpaUIhWhUAEFYJH8brD2PDO4liqYUBl5eXQ90A0mQODw9tb28vE6I/jYL8WMgTBoqEmEKeEuBiAqsCxJAoCKJHzs7OMlZKZTQgWLfbDTt04enQLe5QNJrNpo3H42BlpZD0+fm5mVkQRPLCb/26wms5HA7txYsXISXIzIKio8aaaecgpYQ85BnTvmfS81jjeJawVBMSTA2mWChuCm/ZTnZtbS1EpUE8MaKpV69UKtnW1lZGQFGBHSaHVxAPFPUyKKLY7Xbtp59+ssPDw0ytDWof+bD+acYrxjRSwpMy/iJB8S8mFPv2+Wu0TzrG6uHwY++VXcXNq6ursAMUqaXX19ehDhCGB4yaNzc39vPPP4d5AP/VYDc3Nxc8usfHx9br9axWq4X0HVVsYvMUo1dACg90fNS74/G6CECxJjITzzfGa4yirG31gGu6EwqARtRg7ILPlUp3BWZ1LS4vL2cUAmjn4eGhvX//3lqtVtghStOE+YZ/Xlxc2C+//GIfP360Wq0W5st7jFOCsv5O8Y5pDJ5eYfYC6VPw4Bj/9REfKrSbWZAxVMnGoDYej8NOTKT6ac2a8XgciuY3Go1AU+FNROdonSGcEcvLy3Z+fm4rKyu2urpqg8EgI4BqSDeRb15JVFoQM9JopBeAEUBpIeuW+4bDYShkzlzrDh2k/pjd8e4iIUZHWN/8H6MpMQONvz6lENLv8XgcImfoK84GjSbEe6xjzfohglyN2GZ30TKsMSISeTfzy3PVaMcORLppgDcIaVt8WlcKX3XOiwRoNf0k2p22q1JGO3WdqmLNhhMXFxf27t27EI2LvKp0SqODVInkHMZrjXpTmu0BWozxG95sZqG+VEruj8ng8GjmDFnYj52/NybP+X4WTVO9AYZz/hu8UUONtlfxLWXEzJPZNKrs4uLCVldX75ViSMl++lvnGJoeq8+ZGos8nTk2RzEDpT6P8VA5oWjZxux+tAdt03fG5u+hQFQ2aU/o+lr2BNoKvVQDt0aAE1FDdKdGg1Neo9VqBZriZZu8eVTwvOFz8cfLSSnINdTkCcwpiC08jj2D8IC3UFNeEH50Fx8tuEhYLblo5XI51DRhd5PLy8tQuwNiSUQHOxXhsT88PLR+vx/dT/2hY/EQUOb4lAKpIp4yZj32VksvAGlaGXnV/j0KCHZsD0k9GiJrdBvR29tb29/ft48fPwZDCu9j7tWzOGmsVCkleurs7MxGo0/V4VdWVh5kkPPMJKbAPFUUhmfoZhZqwlQqFfvHf/xH+/rrr21nZ8d+//vfZ7YmZAvXv/zlL/aHP/zB9vb27ODgIOMd8MQLIwzpaBRiBofM7vLiIYqq2KBwqqCPMEYtlIuLi4B3p6endnR0FEJM9/b2QsRVzJPlhZJpaIxnPHqsH11vRQKMxAtRnvjDkP16xCDG+IK3agDwwg/v4t0+UgCvBsrj5eWl1ev1UMT24uIiFB0GdzX6jT7wzbMxhJ+enoZILy347JUDNS4BPsVJBexphK2nSLUg/Hxubi5syzwaje4VEiUKqVQqBeUew46nOWqoYSy9MqTGgnq9btvb27a9vW1bW1t2dHQUFBkMnj46hvkfj8dhHobDoe3t7YWUFZR8szvh1AttMeVej+kHffLXeEMAz/Zpfv64aFz0tBzjAjA3Nxdkhmq1av/6r/8acOXVq1e2tbUVdtVjC2bqj6AUqHJ+fn4e1oCmV1H7a2FhITiWVldXQ00jaCzpcKXSnScSulytVsP8+qhWHX/GEPquY6u0j/tZUzqPPvVFUyfn5+cz3krqDGhUbJH4qLWUdP0QYc14mN2PBvEKLqAKnbZX6ZMfKwquV6vV8M7RaBRqZOi7x+NxoAdaA0UjGs3uFHtkYGQkT/ehO4uLiyEVc35+3nq9XmYHL+WXujag/Rh1MbypPKo4/RTKYafTsePjYzs4OMhs/qBjonyDtak8jLRbUpTG47EdHx9bu90OqZ/KZ728ps/3KboYGLTwt+fjo9EopJ9S/4t1pPU4KPqfR+v0269b8DWmLyhe+ufQV9ri7/1c8LyZ5+u688Y1vca3NSZjc30qoqVUKlmlUrFms2lbW1tWrVZtdXU1yK2MW2r9Kk+mH2xgogb5VHtj8+rlFs57fun7zW+lv7G5Llq+8eNhdmcI1fbF5ExPw2L01fcZPNMIYdboyspKSB/mHujiaDTKOMRqtVpY1xcXF9btdu34+Nj29vbCpkCptEMvj+v5lB1kGl0xtRZUxyCFLc8pnGuoiSkK0/yfpyTldRQC5IuJwqBU6GSyyPscj8eZImwQcgw+Koh4Qgciam2BmFU41ub/myE2f7E+e4JJWJmG9/sdd/yzETapcYPHx4dq81y239Yis1z3mHQ0mIR6u0ivYq14IvlYSBGnIsDjlkYyKJFD8dIQ6VKplAkrjBEnZSg80287imDI9bHQYRX+9bkaSoqhrtfrWbvdDkSVGkRsKazzHjOmpQSe2NilBKI8RlskpASRmLFpmvUzqX3eAKTv5n+ld6TVaI0VFP/BYJDx8ivj9u9EWSHSg3XoGeWk+cr7/muBKkpKjzSNBUO/eg8Bb0yLCTw6rl748cqjmWXCvTHSeHxJCbzg4cLCQogG0qiHmCAZe56HGD5x3q/3mIDq31c0PVXDz2h0t0MI44YzAmFxY2Mj0EOKsmsOvT4POgd9xLFERA3RMVdXV5n7WD+aCjoej0MkJJ5CTTvS6zXaUMc79u3HV8ErF/QjRtvVyEVfPU94LN+eBLF2cmyW3VI2Bt5Y449jY6UCNvInBhMtQOvvTykrOi8YJ3wb1Yvtx9DjOOMQM6jEcJH3ahqBN3Dn8cgiQI3Lmkqn7061X/ml7oB4e3ubqc8VM9LEnq20CDzWaBpvLFMAHzWiCflSoyRTOBcD6AsOUe+s8us2NU4Pod1FQgz3HqI3psDzDmRWjXBTx/606xe80TnnnN/pS9udt5ZSNDdFk30/855XNExLn1P0wT9HcVSvx7isRZv5aIHhtbW1QBPgyeAm5z0uINeSgs/mQQ/hP9PwjGnGRscnds00ODkx9UmR4SENik1OngKiHfHeeSxNCE14kjDO4DXgvBZh49lqtDH7VGUeSxzbzLJtX9HCRAo8gX2qd+bNg58TLwD4+UcB6/f7dn19baVSKZPK4p+j6yGV26n34JXo9/u2u7sbDDflctkGg0HYBeOxHh31hJCGtbCwYLu7u8FbMs08xPrp/y9aIOW5nmgTHvvu3Ts7Pj62arUa0hnMzOr1evDIEp2mOxb4dqrxBwMNW8qCb3oNRh/aBSHV8QFggIPBwLrdrnW7XWu1WqHYF0XbIK4xxqhjkkefPA3wY5miVbwLAekpYJIAr+Hd/I+yx70+MgFIGZlVwWf+xuNxMJb1+32rVCqBuWmUIXPhI2lorwrWjB1GGYTnUulTGk/KKxubo5gQqsdKo/gda0uRuIgwzjhxjmijs7OzUA+K/vv6IhiLYwqsKsaqZDL/4HG73Q4CaavVCttOdjodu7i4yIT5xwR13kGNsdFoFO7V4sMaveTnKTZXMfoeUyRivMZfo/hZNH9cWloK77m9vQ27mmnKBXyC//DMMgeMM6k24BjzgxK/sLBg3W7Xjo6O7M9//rNdX1/bysqKbWxs2NbWVoiqIdWCtEHo9vr6eqbItBrm2u22HR4ehp3F4GNm2XQ3j18pw4Snp2qU0SgPZCkim83uDF48h2s0yrLodFL6p+tDf8fWT0wG8uBxB6McUSh8iODmvTqOy5mRZQAAIABJREFUt7e3wdkUo03aVo2i1LXkDTR6jBEB2qAOEAwWMUXS99Mr/+C+Oke1fUXjIpGzaqTBwKFjqvKbzq/KFaQKkfLgNxzQ+VSjIs/hf/D7/PzclpaWggEeB2OMrzA/yKrIl9AA3wYgNjdcq44AxTkvv+cpyawvnd+i51HlNI2W0f/8MW2MPUvbr6C4CKDAI6Nq5KA+z78z9W6iS4nYQM+Ztj5NSiadBmK6De/zpT90rIsE5kjpX6wejNIFszgd5tvTkoWFhVBAGGeIRolSGJy0eXAIQzjOdjWaaluRtahVpIaaFL5MA7E1GRs/rk09X519k6IUp6pRk0IWGpBi9tN2inth5NRFUMZA7m2j0cjUxsDbf3l5aaurq1av1+3FixdhUenWh7oDCIL2YDAIQo4XcGNEuGjQZ2pOdZHg50MVvZhgpos+5d1BgOQaRQCz+/UQNFKKEGUN/+a9zPVwOLS3b9/a8fGxra2t2erqavDqT1M1Pwa0T3OOfXuneYaHmNBQNOjY6vNRGOgT6/y7774L7cELSwQRVc89MeE9nhhDVME1jUaDgWnKgDJsfc75+XlIn9nf3w/pWP/5n/9p+/v7dnJyEtJkUGZT4+2PUwYXLwD5cFMl2v64VCpl+lUEwFi819AbWhR/tF3gjKYnKg5p+L8XlhR0TNRDiEEGAx0h/eANoIKDCtPabl0nsTSpVAhySrFQg4HHuVi/SqVSMOIXiZNaXBkl4OPHj8G4qDu/jMd3tbXG47ti3+Ctp5tcx3mlc/Th6uoqGDhxQJAyCP7EjDReWVdDENus49XWXadiipEf+7y50nWuc+6j8nyNDt/uovniF198YdVq1Wq1mpmZ/fM//7P97ne/C7sXDodD29zctGq1apVKxba3t0MxQnafUOcRCsJ4PA7nNI3Cb99cqVTsxYsXtrKyEsal3W6HdGBqwWmE08rKSthVz8xCNI3WYPN0T6NFNXonRi/VQ6nzRR+U3nDN/Py83dzchL6yxufn5zOFktXIURSo3Kh0U8chRSd8ZJpfX1yjyhFyp9ZRQKbBaDIej0P0U71eDwYd5E+l/yr74JSkXV4m8UYF2qNpQqTq3dzcWLVatWq1as1mM4zH2tqa7e3tZZQWHGPQI6/0oOQ/JRAtosqbGvlihgdvxKI2DPdocW7tk5ndk2HM7oz8yEoYaDHS4kRUQ7uXI3gO87W4uBh20xsOhyG1eBqHsLaL/qbSb5RmxuSelBGoSJqa4gtevkrJnSnDVart+hvcwWEMnrVarbDWlQZrG3xKMPMLvpVKpUxpjWnGwfMwNWTodfqdJ9t4Pohc/1SGGt8uf47jmFyq98UcjmZ3qYAYZtglzczC5j7gD/fzTs0GIEhAnWFzc3O2t7dnv/zyi719+9ZarVbQG1P4ltfXz7EDpK7V9R6TAT08WAvJM7zEGN00QKPV06rIBhOkEJ0qAWrM0feCgHiA9Hqzu7xqtbyB0L9WRE1qHIp+d0x5feh9es63c1pDHIYdBMJUUS4QHMUQrzSpU58TQu2ZBcRCvYYPgaKVhxToWKcYFUSyVLorQIqRhQ+CPyG5MYDwqpFNcc17K2J47/GRdoPLpAAcHx9bq9Wyk5MT63a7wVCaCt9/CKTo0SQhQA0cReOiZ9x6XudYjah8Yud8O2P/eWFJ3z8ej4Ohj8g1mB8CZWwcYoJIbHy9kSE2nimGCHwOXXwKOq7G0W63G4REhAGEPRUmAE1Zecz6go5S64uIGow0Z2dnmTRUPz+xeWO+UdpUCVGFPG/uYpD6L7ZOYtdP+v9zYWdnJyizS0tL9u2339q3335rlUolGGT4n6gJ5g5DDQYxhH8VxL2CqXn4RKOa3UWpQMNxIoGDakSjyKqZBa8vhTM10jRPwPe0AcjDM78GMIrE5sQrZirXpYyzj4XU2owpO17GmAZ0zHiOFrDEG+xT6/V+5aEx2cnTfS/b6PipTGxmGSMYx0T6DQaDENkH/6ZmjtIIaIoaafxY+XYWDTGvOGPHGKTWJ+OmdBWaPCnlKUUTmSs1hPoCzSn5RO+7urqy1dXVgP/T0DD/LD3+XB7pZYmngEltSMleRQH4pqUAtF0en2Nt87qn6qTT8sE8fTjV97x26dr0RsYiIcUfOJc3v57uKu1Q2ROHMvUvcQhSgHs4HIboGWQQpaM8h6hYHQv4M459HwH319DtzeL8dlqa+iBDTQzB8hi1XjMNAfHeBq8k8llcXAyeMLxYFHbUUEMzs0qlEtqg57X4m1rLU0LEU03uUwqiCikhJU/ho00xxuifE3tfqZSNLqGYIiHePhLHK5LqZcKLmRd2Oi3ouqpUKmFNPdRr5OcuRtSeAvKEacYPz0upVAqGkXK5HIR+FW68wFEqlTJ5o5NyfGMMSSNBAK1jQHHpg4MDa7VaoW6DJ6iT6Mu0hC5lSOBeFVCV4D9F6lNMaeK8KkIxvPTGGu0D/+u93uunzB4cGwwGoSr+YDAIinu73bZer2cXFxf3FHhts++XghfyvfIaGwPtT55gmqJjGjr/VCHeWoeA9YOR0Ss8Su/MsvVLYkJjSplQxXg0Gtnp6WlIK3v79q2dnJzYyclJsk4J71GjK0IRdFU97do+P9aThG2Po36u/f16vRoBUkpLEfAP//APISp3fX3dtre3rVarWafTCelr3W7XarVaECYxdHc6neAFJEoGDycyBlECKPYYQbVOjUbRUs+J+lzspHhxcWHVajXgaaVSCfIKKaPtdjuzw4+uF1V4dcwVvKKXNy8A61lr0/jnq6Jtlt25rAjQdRRT9GP9SslC/I61X8cDuYEizo1GI3jqfRTIzc1NiLrx8oU3Pig+4pDkOhxVmvroI4lIYWy32/bmzRt79+6dnZ6eWr/ft9vb25BeQMFdpVF+7LSNtI/36PVFgdaFMrPQTy+Ter7oZRkvU8Zkm9jH7H6aIAavhYWFYKiBH8YiYnTNgcdLS0uZXYcmjVuMh9LXGA2OrXH/PK7Xe5/CUJOneMbwL9a2aa6PHXu5dXl52arVqm1sbISUVTWUebzmWyOEccYQNasRcw/lRamxjp2PzQ1zpmUHfHpiUZAne8UMydPo9wrM0+rqqjWbTavVatZoNGxzczOzazObWywvL4cIJww8jE+z2Qzbpl9dXYVxwWgN3YzpjHk6rr8mNR4PgTy8nkRTpzLUTKsMaYNii80LpV5g1/QKDS3Vuhhmn4j4xcVFCMkfj8fW7Xat1+vZ6elphmEeHh6GqIL19fWgkJPLS60MQoc9I/g1oWjCmffcPIEsdl3e8ya1G4XJ7C41Q8fYC0Y6XysrK6HAI0LuYxUvT9Dr9Xr4vbq6+iiPhz/WcOai109sXfo+cQ6lwSy7e4vflcP3gbHVMG8Yl/caKoHRFCH1/qmCfnJyEuqe/PGPf7Q3b97Yhw8fwrZ5DzHAxZh3TOH34KOBONZIO4xTFDQrEsivpdgz7fB9V6Fdx9KHmOYxG79ewDXmnmeRtnNwcBA83kSIEFaqcxoTkngeoJEYhOWzTtS77tvFXHgDE89XY4MqFxpyG2tfkbionjWvxOARAogM1Gt0G1rFIaXFhPeORqNMzj1h+GtrazYej0PRZ1KuiOTRVA3GIc8QTVvU84+BSduoaylmYIopEIpzupMLQhXP1NBkbYsqUEXCzs5O4EvNZtPq9brV63V7/fq1ra+v2+XlZaCByBSdTscGg4E1m83QD3iU1qHgPNGLR0dHdnFxYUdHR/bdd9/ZcDi0arWa2TGR605PT4PxWusFMQfQJOadj867pilBcxhDxX/erWOreObX983NTRCW1Yh9e3sbFCTAG7nV2VYUaGF7pSmq5KNcEXViljVO8NuvW57DWPpoDaKZLi4u7Pj4OBhId3d3Aw5fXl6GtcLOXMwJeK14D41fXFzM7PSEwQ6ZVfEExQWFZTAY2Pv37+2XX34JhgWUPIpgK83mvO4S5xVZFFhN3S0SGMeLi4sMbVEar84FpZc+3dfzO/6j34y/l5sYe/pJShhGeGr/EGmKQ1nXEcazfr9vJycnViqVbH19Pcwj+ofnS9pOLw/HjDQKHl9jz9MPfEGvKQIUD1PP1f7GDIX8jsnYej52PeNar9etVqsFJZ+IRdKRtdi3Pp/n8lG8h4Z4B4IfVz/WZlmZU++L0VnG0cs/SjdVripy/mIQG3MvT+m4ACrPKV1Drt7a2rLt7W376quv7De/+Y29evXKXr58Gebm9vY2RLoiJ2hqE2OnBrTr6+sgV7dareBo9OmP2kY9TsnPelyEHKlzDv1VGhaDYgswuIbEIE9ojg0MRAXiiTCEEYfK3uR5axVpMwuCs1b/VmMAxqHY4vt/BVL9yluYXmDh/9i8+nN5CqOOc4xJmVlG2NKdoXxdmccCBYrZih3hZpq6NzEGErsmDwc+F1LMWEGZnyqWXvHnWv98lCq/M4oKUXn9j/2P0Hx5eZnZ6tlvu100+HZ4RSGldKZC+z8HPBP2cxkTuDzE8DQ2btqnWB8BImj6/X5YN6PRKCiZ6g2M4TDHMeOKv8a325+LHcdA+/PXoNUI5GZ3kVdeYIvhii+Ep2OqoEZRjVjwUWla8FLz8xUmCc2ePvt7Y+uN49j/qW+OY0YBBDk/ZjqWRePi6upqoD0YkPCissNZqXS3U97y8rINBoNgFMMwAI1kPokamZubs263G7zrKPT7+/tmZiHVEPnj8vLSTk9Pg9OJNCe27EZ+6XQ6ZvaJjxGZE4uiikWDPARSfNwbiTXSIlZXjDlVxaxoiBl/dO2leFaM5qTkGTVgIn/qBha6m6LyT/8Bj2P8hbGCJqOEYBwgnQfcMLuLUiLSq91uZyKyUFCRe3QsYjyIdnjZL8VnigA1XqtC6A3YMfnV0yRPn1KfWNSevovxYi6ur6/DePqUKt8uNm1YXl7OGHtSKVOpb+2Hvo/3TJIVFJ5SJn0I5OkQ066vGB6Dc2aW0RMxiPrdSic9l8g1pa15UQ8pnPLfsfnPO469wyxroHoqeMx6mSTboXuTNko0Iro7NFZ1duZVaSbzrbtAmVnGcRyLAIpBni48qW/TwENk2xg8KvXpodelGJ8HBpuPF9gQUldWVqzZbAaL2/z8vD1//jyzlSaLd3NzM0yuIio7/mB9RVj6fxGmNc7435PmMUV8YgaAaRa49zT0er1MrSI1FEwDMeI/Ho+DAnp6ehqs5g/Zui1P8XlKYcYLnOCFvg9GQuSRWbYSuxbUjrXXCzFqufbGlJjwA6j3izaDY3go8A76CJ+HQGq9xgwvMYHU7M6QpdEkasUvEmIh5qqkpoRSvV/nZZKxInYepZ93kfqCx5k5ozipFhv2wor2h2f79qTmNk/QzTuOPTu1Dp5KOUQpVSMW62c8Hmcih/DyqHFmOBxmUhDUM+uLl6LgaRE9FHs8Sd1uN5MWapZNAfNKT+w4Nm+xMfcQ4wt+LaeUC/rtx481rkJo0XO4tLQU1jORT91u1w4PD213dzdESVSr1bD7BLVrKJ67sLBg19fXtrq6GuQLBHszs/39fev1etZqtUKxUww1RNzg3dZi691uNygJFInHcUFe/s3NjbVaLev1ehkvv4LSftaAjqfnkbF1AvgIGo3u0+go5l8FZn3GU/FHBdrjFX3PkxQ8bzXLbuGOrLC4uGjNZtOazaZtbGzY8+fPgzxZq9WsXq+b2af1hXxJJJwqlClDDfxxMBhknFRqEFajD7So1+vZhw8fbHd31/b29jK7mEIvjo6OMqUFdK6m4T+Ko0UCBgz4LzzR8xdtV+o4JSf5Y32u3kc7MJIOBoOAf6q4K3hZGPlmfn7e2u12iAb3O8r5Z8RkKX+NN9iY5UcbejmB/uW95zGQWjN5OsY0MnNM6feRTODE4uJikFcoTK1RTJ6npObAzEJNTDYDiMmDefMVO+f5q+fROlexaxTvHyszTwsx+X4aOTOGh5znP43Q8/oEhhmVg+B5arQxs2DcwdhDxCQ6fZ6h5iE0zPOQGO/01/pj/xs8VOd5CnINNY9B4mkWpz/HBIIY7Xbbms1mUO7IBUeZJlSfUNJyuRxC3BQpzeye8Me7+v2+HR8f297enu3u7ob0p6fy6qfAI2WRhBPQfut7UwvsoQqvZ5ZeOASUWHpDHM/RnH0l4o8xgKQYBIiR2up42uem1rp614sEP84qVCMwK8SIRExwZT78XGloZal0V+MJ5TFFkPxz1Hh0fHwcqrGfnJxkwvrzCF4KUswj716uhwGyXlGM8YyurKxktl4tClQwZpy10LbOkdYaQvGJzaN/ro/agcnzPgpoE4rqcZOQf96vbY/hNPdyjY+aUOMB61RTazTSSOlHLAyY45ThQQWypaWlwmm6Kk4AAhW4oQLC9fV1aI9ed3t7G8Ky9Xg0Gtnq6qqVy+WwAxcpeRsbG5mdDlV45Ll+zpQXxniAriN/DaB8NEXf/fwoffHX63OgLZq2xXrFOYMgXiRcXV0FD/mrV6/CuH7xxRe2srJiW1tbGUPZysqKtVqtTOHeubm5kEvP/BGlybyPx+NQfJidgDY3N4Nij8GGCBpSajRqihREomtQzknF0hQqs/sGBuZOcY550DH3uKXnY3gJKC2L4cVwOMx4SIsCjWzT9itPidVr0uuBmNI0Go0y9esWFxet0WjY9va2VavVkD5NShje++XlZVtfX7eNjY2wq4nnpzrWvAuapXSBiA4MOESPm1kwsF9dXdnh4aG9e/fO9vb2ws5hqShIs2x6qioKngbQTp27omVkPOsoXrxH15KfF13j2maVF728GuNXMdkFwzrPgV4T1aS0MMZfmJPxeGz7+/uBHmCo8WvP9yXWL22jzqeXgfS8N3Tp+7zR4nOBZ8Vk6ZhSq3KCd/7EcNTPrwK8YnFxMdQU8/LBaDQKu0Bqu9TppXNZrVZtfX097KBWqVSs3++HjTq8jJUnD3vaynjpM2L4F6O/pVIp8BKyQooEb8hSnDG703E49sYkvr08ypjd3NxYt9u1SqUSdpnEycA4IH8qjQLy9Fj4IjvbpnaO/VxIzfUk8Dir/CmvnY/e9SkPkSY1MtVwmK4OrioQqiRqjiULV8NJaZOGl0OYILzUwomlPv3/CHkKb+xbj/PGLib8ecOAZ1yeaE4zN9OuQ73mIXM+LXI+pB3TwmNwK3ZNjIGYpfOc9dtHD0xLoCCeFxcXdnZ2Zqenp8ELnCrO9pBn54Hvj2eqMUELoVRTKIsE7a8fYz3Oo5f6jJihJsbAVGjzhRp5J1E0MGIfTZNqj9l0Appv3yQGNemdfk3rO1X4KRq8B0QFkVhbVZgxu+8ZUsDgosZSrwRWKhU7PT0NdJTaESgUHvLwKSbwx+Ymtkb9uRh/0N9eacp7vvJyn4ZZBOD4YZ3gKDKzUIDw+vo6yBw3Nzd2dnYW6mmUSqUgKKvzodfrheiYw8ND63Q6dnx8bHNzc3Z1dWVnZ2dWLpft/Pzczs7O7OTkJBSCVS9+uVwO7bi8vMzIOmZ30aEo5Kk5jgm2qsjqNZOE4Bi+md1XtMzu8E/XfdERio8Fvz79+ZgyrOn0RMmsra2FSBkM+xSWRmHE0JrnyUdgh66wHqHHGAhYo4xtr9cLRabfvHljBwcH1m63o8Vuh8NhpuC5KlPeeBdTKKal2Y8BxSN9bwpi+oeejx1Pkt9i/FK/NdLf44t/Hg7l8Xhsp6enIbXLp01NavMkucgr0B50/mJr7ql0nmnkt9Q1Kdz01/j5Ahhr5YPwkGnWAM/SOYvJP7F2ptbZQ5V55su/M2ZcLHoOvV7gz8doY+xe/0wzCw4PjGCbm5tWr9etUqmEOm/j8Z2TRvV/fU4Mt32Ahtl9eTnFv54C8tb3Q2FqLWRaYsi3n6wUUvlr1KqoxYX1m9w2DC8ItngdFVlU0VKCtrKyYo1Gw66vr61Wq2VCsH5NmFb5/xx4zFxMalOMMflnqsADAmoEgSLRNIrEtDCJyX8O5LXTKyNPuZ5iuOaJtxJ73648QTUWHjoej4NX14cOT5o7BEyKfbONMIVQvXL7uXgQa1uMiOs1eOiUoeQp00WA9lMNRipAe2FKIxL1Pz3vwz29kcTMwvbAnvF5Id6/R9cV4xY7Jrxez+v9sWPtY54CGbvX47x/X5EGG10Xmorg2+fv4Ztj9e75gq9qqCHNhEKt8/Pz1mg0rNVqhXXT7XZDVI4W5fU0KabgpPhBnuCfJ4ym/vMf703VyA/l6d75UhRgbCmXy6FOWalUCh45DC4o2uPx2DqdTjDCUCMPgZ7jg4MD63a7oR4NO/GYWdjZqdVqZfrkHVQ61uPxONBdoqwYE9Ld9D5vdFdcUUWTa/R6s/trY9IxEAvfVvnrKXCRcfLHPpJ6Uvu9rBJTwIjOW15etmazmUmdL5U+bUjQaDSCcWZtbS2sHT8OHhgbFEyixq+vr8MOJiiNWqPo4ODA9vf37fj42L777jvrdDohtdvLZB8/frSzs7N784AHW+lobB2qEanoOdTaklr82Y9XTIn1iqXnWVyTOk7RPDXU+NTvmNKuoFHb3I/xTdflY5W5FP2e5l7ueSpcjOFOTEalHf6aaZ4dmzfGhBQzogyhm57/pN6BzHp5eWlnZ2fW6/WC7Ku7Nfp2xfodW2vT8GA9r23XCFOtkVc0xHh5qn2T+D99MPsk89TrddvZ2bGvv/7avvnmG9ve3rb19XWrVCr3+q3pnbH2wL+g2xwTBRnD1RjvSq29GJ+YRq97KF5PeubE1KdUp/KIRGxwpmkMwtny8rI1Go2wEwOeC7a60+JBCKWEni4sLFitVgvMVhHLe8cRzk5OTkL4cKrt/zdDzHOphM3M7v0PpAhprKCg3uMVOi3GpeHK+j5db57I5c3JtEhXBMTwIUa0nxpiCmxKUdX2xDz7zIu2nYgz3anC7K5y/ng8zhhFNfxShQAiaU5PT+0Pf/iD/elPf7Lvv//eTk9PM+kjnztnKSbpmTt9gXlrOPfKykrY3WV5eTmzY0BRoOGcahxC+QI/FG841rnz6QWKc3lMh3tTHnj/jDyhVBlw3nmlP14Z9+G+zJFX3rkmdazjpPOLgl2kMIOwpEV+9X0+2kwjY1TRYaccHSvaura2FvpOjQTGkNBhCqAvLCwE/sgY0t/FxcV7Spk34qXmKpYKYpblH35uvfKvRi3lwYwV58EBAHzg8xQh3t9//31IKdna2gpbha6vr4dIBlKa1NsPXaQg4ubmZti0gJ1hDg4O7O3bt6Fgfb/fD+NPipfnb1550/HX4tI6Zt6xxVyorKO7/rG+fNh+zLjDc/U4Fk1JFBfnNRWPdf5UsLS0dE+GYS3RV+Uxftcn7lXaqnS5VCoFw0u1WrVyuWzPnz8PaXFsHXt+fh742dnZWaijSMFh1ocWE/Z8GWMd+InCyXbuHB8eHobo1Ddv3li73bZ+v2+dTiesWy/f0WdfBFf/m6RM899TyDiaEkS7Y/X0dLwA1hcKNm1TnqA4ZHa3AxTnuR8aRhuYF9a1GpFYM7qWPN6w5rxxy+NyKtpK+Zz+H3OkcezlB/9OlYOKBN29xq8txbPYseoTqWuUD+EIot/g287Ojn311Ve2vb1tjUYjpOvrmOUpy9SdwygKPuIoWV1dDcZTbYOfE+V5Mb7o17d/jpaA4HmaAgy9f4qob49r0PWYLuh1Nl2rGF1V/mHb9LW1NVtdXbW1tbWMPMH7NSrTr4kY3VJcoxYXNbpS8qs/F5ObU+enGcNJ+OX5b57RdOIMT2qoH7TYf7FJ5XxeZ5hgFiO1I4iu8ZZS/a1t8ESRZ/vIgb8VA03RBNQj3qTf07YnZizw1/q87Nj9fp1Masc01xU9l0XPyVPDNIQCiClWajDgeTAyb+TIU/gx1PR6Pev1eiHHN7Yd9aQ2pwRJ7a/Hff9NXzleWVkJwl21Wg1CNQbhopmgb4vSLRWwYgKfF868gSb1Hh0fhbzx9M+K4XiKtgIp4TPvOPWs2PnUvb6PT+EBBnx0Ge9MjSnXo6xpoWClp8vLy4HvgWdEflarVdvc3Awew9XVVWu1WpkinHhuzSzj1QVi45Fqs/YvtQ70nPJYzmm//cdfb3Z/2+WnoL/9fj8orv1+30qlUjCieOF7PB4HQxF0gZ1+MBbc3t7a9fW19Xo963Q61ul0wna+KQNBCvyYxpSdWDvz5B3e7fEyhXN57VKjjVm2xoa/XsdQhfAiwLfHg0bW6PUc6/2x31pX6OXLl1YqlWx7e9ueP39u8/Pztra2Zo1GI6wFvMIYaJBZ1fCTJ09hrCHtTo01bBF9fn5unU7Hut2utdvtsKOYT9GYlk77sYxd4xWiouWr2G5WeZCitd7oHOvrJF7kxyrmRFCaBs1NyZ3eMB6TSx4KKR4T069UltD3PYW+M2mdPXTtpebHO591TVLLi13TNCojD/f4YJBTnNMNLzwvS+GbHsdkT5Wf/Rr2Y+iL6ypNfQpDeIp2xCA2rvSP/1ZWVmx9fd2ePXtmv//97+3169f28uXLUKttdXU1oyum+FqsnYy/OsD47VP8HwJF4keKNkwLj4qoybs+dZxnjfPXw8wRorRQFILR8vJyEHaxTC4sLIQ8Nz+xMWTgXow+2o6/ltHmKQRSb1B5CLPNY94xUMVyPB5nPMwwOa1Pk2fcSxmAUkz6bwU+Fyk/9915v/1/flwxwHgLtzfU6C40ecxPDTXn5+fB8xhTnh8yl9POfUwQ1+O1tbXQp/X1dVtbWwsRfDDtpwJlRLFoEQRA73VRmOQtSAkPsWv9PSropQSPPCUg9r8a1FIMPvachx7zW3eNKxI8fgA+shDA+4ZCWy7fbcuM8UW3dQa/UPzm5uZsY2PDxuOxNZtNe/36dTDm1Ot1293dDcIkO45cX1/b1dVVUPx8+xU8Pc6bn5gykLpe1zf3q4dTvYpqFNVIKvWYFwnUgZmfnw91P3RHHKKxULaXlpYC7yKlBXkDI81gMLDT09OoAq19yDMceAHVfxPx4e8zu5/i6fFWlUpvtAFSa0CMZIxxAAATJklEQVTnWqNOtH0+MgUvMF7ip6CpXiGnjxo5BL4qb/bRQp5eUReqVqvZs2fP7De/+Y2Vy2Xb2dmxV69e2cLCgq2urlqtVgtFnjHqsDboux9njye0AyWRAtJ8MNxcXV3Z5eVlMAaSQuxTnXjHJBod+52CpzTU0H5kD19EONYOjlmPD5lf/7yYLKTKYl6Kq197Ztko15ScE3unfsf6Hjvv+x2j5fo+/f8pHBix9qfkFt+PaZ/tDbMaBdPr9axarYai70SfcF+MrmkbMJZS2P38/DyT+ubpc0r2ULqkOmksssv3SfUorce6uLiYaetTFMvV/qgzRde00lR/rxo16WOj0bBvvvnGfvOb39i//du/2cuXL61arQYjN8ZsNUDFaFeqvd5Yo8XYU+mKwK+lq6XkVdqQ145cQ03Mapl6Ef+rQKDndQLyGvfx48dQywLGx1ZbV1dXmUWDFW5ubi7skqHIEQvxVuIJ09OCmb8mpAjotIzzIe9RxU9hEnFPgfcKqnDLtmhXV1fWaDSsVPrkbaQm0OnpaWaNxNaYX0Opfk3732ORcZJQk8KDSe37nHaknj9JafcQI4bco2HY5XI5WMTx6ithzRM4VlZWrFKpWL1et7W1tbDbEMxOw4fz+sxvLxil3usVQ888EMLZ3alardrz589tc3PTnj9/bqPRKLM1Y1EQq1WgwiRtUwGR8yq8+uKPOgZeiNX/8s57IS4lZHmvkJ73x/TD47UX+FMCthdAU8cpeoRyXbTXyad0eGEhFmGga1EdBYyVepWpwYY3HQGyWq1at9u1H374IdRUOT4+zhTiW15ezuysdX19bWZ3866pINPSDD+2KSUg9RzmR0O6dYxUQPWKkRZULhLA7XK5HOrVzM3N2fr6epgbjGPlctk2NjbM7NOYNpvNDC3RHSsQoH3h0EljFJMHvHDvj83u0w/apMcxuUuv8TTD46LnD6pspCIgVOYDJyh4XRSocuMV4xh99fRD5UlNgdG0gi+++MJevXplOzs7wQtMLRrwlxD+UqmU2UUv5jTk/TrfKBbr6+tBKSTNApmJKC1SnKANfncn/+yYbJmn/MTOY2TWNLoiod1u29XVlZVKpXs759G3VBSgGnhS+oo3xkwTVabH+nx/r75LdQ++1asfi5bg/Zruoam/qV2i/PU8R2Udznu+w3bHReo8rA3FP22PKvlevvfR26nzagRW+rK4uGirq6u2sbFhtVotyJmk6g+Hw1CMnzGL8TCNivvyyy9tcXHR1tbWgiEfPCUtyssySh95po/A0Y/Hw5h+pnTEp7o+Bfg2wINvb2/DGF5eXob2Y6Q2s7DTYalUCoX5b29v7fj42BYXF+38/NxOT0/tn/7pn+z58+ehoHC1WrVmsxnkntFoFGQaxnESUL92dXXVXr58GRyv3W53Ir3z6/GhENPJ/HFM7uaT4qHAxIgafXBKcPcN0+88xcosa0zx1yjj1QgbiJ8qLlr3QdupxFsJPt4JtsL0BfmeAmILxQtkTwExgsT5FKPJW9QxZgUxWV5etrW1tcA819fXA5LXajUbDAbW6/WmbvNDIA/ZJo2tX6eTnpESvBTpn2o9PUTA0rblnVeGonUkSqVS8FbEjJmxcVP8496rq6tQ+NJbuKftj74rRWP8PMQiaVQ5phaImYXtVhuNRqAHpJc8BXjhM0UL8wTQ2DqM/fb3THuftoHjlLc/dpyiJ37e8hhdCtfy2guoJ6wogNapsGx2t8OgCslmWaWZAsLlcjlExOCZN/s0DqRUUJAUBZPipRg/z87OwnpeW1szs09raDAYhN2J4HHgogr3zIE3cj3m2AubjAGgnkC/3bHZp3nS0G6eozWNija2ERbP++mDj2zCWEqdkfn5eatUKiENmzpAWnvGp7r4qA8gb237KIDY7xjtA3x0jVk2bUrB1wTw7eC/VGRiDLzxTce9SIjVI9Q2qINOj/1Yan8w0rBxBTs6aQSVNwwxdqpcxHhYjGeqUUIjAFGscYwwfkTe+FSnGH+cRIe17alrlO4/BVDLgpo/msagUQyeVnljgD8f62dqzXqZ1hv1uE/nW++NzWupVMrQljx9yEcT+Gu8oUL7qt9+DBT/tP9PIZ8qXdI2pMbGr7mY/BObu5hMgvON/yjMrXODodg7mMbju0gaLRqt44ehkmOzbHF2aE2M5qtMTSQ6BmEdJ7P7UYkxBxhAu4sEb/D3NX5oEziaWs/8hh9qOijFw2P9UDoYozt+rSitQN4hyjC2Y2mKX+XRDz0/6V79nScn6RqZhItT1ajh2xtAfENSitQk8AinyKALhWshfMrwFRm1nTpJ3ttFRA2FhKed0Gn7kgee+ChSPxUB5dszoNix70Nef5QYEZ1g9gnJNzc3bWFhwW5vb61SqZiZBUIFeIVt0rseoqhOO5bT9G/S+/KE1s+FSeMzzbrT9nlcVeXKb5NJrjwh2OoR8O9W4yjEExxDcYwpztMIjHnj4pm4Ct5eCIeZm5lVKpVAO2q1Wih0Rp+Lnk/PkL3gnjLIxGhv3lhMY3iM3Rc7n7ouhRcxHPTrLqawTLPGp10TXKdCf1FQKpWC8qQ8aHFxMShwuouTCoVE+MzNfdqmEgFGdzxYWVmxtbU1W1tbs/X19bDbE4bEtbU1q1arYQcX6C7CE8W7u92udbtdM/vkAaRNXojWfnmFRAUJcIn2m2ULr6KkcowR5vb2NvTBzELaMlGxpGtpmozSo+FwaFdXV4XzRdoe85CrUZdvonYXFhZCkXW80zomnhZpnzz+pgTb1HP8f7H71NAF6DmN4NJr8hR1vZd7ELRJcUopWZ5XFD2PrBPlO/yOGeK8oYa+mWWLduJdX1lZCbXLmEOUwNicxAwann575Up3e+KbY5RB0p9Ii9KUOh0L/+3Xjl6bkll8H/z6LXoOMZqurq7axcVFJmJRI9agG2oMgCZx7A1nnNfjGK9UQ5DnobHr8471Wo+Her1/ptmdMZVxpl26kyLn/Vx4XqtRz97B8hTz6NeI1710HuAxsfNePvB0mXvNsjt0YvhgvJBXeUYsKlNlVd1hDfzzxlB1Hvi1qPVwoCPQR+ZR2+hpKO0gK8FHDnlcfYpoYY3AVFlZDSsLCwvBUKxrTK+BZsA7m82mPXv2LETS4HQijVj7poaamLzpdXytK0TxftK+vZxdtEyfgtS7lE9M25ZcQ41nfjHBwJ8H8pSPGMMAWTlmC8TXr1/bzs6O1Wo1q9frtrGxEbwLhGF5RWyS0aNcLlu1WrWvv/7aKpWKfffdd7a/v2/tdjtUifb3x5SPPHiswcd7n4oAJZ5qpfQ7yuQd+zn0v9WDuLS0FOorvHz5MrN7Trlctk6nY4PBILRHn8N3avxj4z7J8PS5kGdIiq1niGuRofran9iajPV3ksAfE/7N7qIQrq6u7OTkJAir5+fnwXusVnbFXfqvzI9IKx0zXUN+3nz7vVASE5DMskwcrwkKNd9LS0u2srIS/n/58qWZ3YVsDgaDEGp+dXVVeESNCoRmd95gPD3j8TiMGcK5Kgf0V733sfBgL0R4QTDG/FWoZI651xse9DygDN7vQKPeddbH4uJiVMAmhDomPMfoV4yWqVKphv4iQHcCur6+zuRYAyokI8z4cGjqrRGirR5zxpU6KswL6YOj0SikOCHYYRQ1s4C/KvRiOCmVSpmdE1GEGHvFRcWpFHjei3Cm404oOv1WR0xMxiAtiZ0dzazwUG+NzGXMFxcX7fT0NHj+UMbZ0QQaAr1YWFgIUaJnZ2d2eHhog8EgGEFwSmiNIO0n46+4YnY/VUPHUseBeWR+Oea8KgNmd8ZE3qV4qYqxHite0i71ECuOEjFG2z9+/JhRXsrlcki7KAJWVlaC8nR9fR3W3XA4zOAHoOnz0Fw1PLE+iaDZ2Niwr7/+2l6+fGnb29tWq9WCM0pTnGJKpkaDxGQovR5lg11nhsNhKGY6GAzsp59+sjdv3tje3p79+OOP1m63g+NDFZFYxEksBcYfq6MzZrAgwghjgUbIFQHHx8eZNGP4NWNklk399YoaxzrGscgEnXezLK01y/Inzy9VcdZrlBcqX8fABk6oXKTzosfaBp6jymYqlc7v7uSNUeVyOaQBMSaeZ30uIONpPxhvwNM+fx6exLHKQswbPF51P414Az83NjZsaWnJlpaWgsFB5XKVQ7WWnWZu8D8yBg4F6DuOBk9nPX00yxqBvZ7n01NjepGZZXY1U35aJGgKGylj7Li8uroaogwp2HxzcxPKkLATpZkFOlqpVOz169f2u9/9zl68eBE+PAc9Xh3EXqdI2QzM7tbZaDSyRqNh5XLZvv32W2u1WnZ0dGQnJycZ2jBJX/S63iQbQOw5KX3RX292t/bzUoJzDTU+59A3NqUYpv7X8/5aFi5h4Kurq1atVq1Wq4Wq0KpgKYHxCmnqHdomBmZxcdGq1WrwpA8Gg6in20NsHKZVBvzi8wS1SOJpdiecpepa5M1TDHx7dRy//PJLe/bsWfD6Pnv2LHiker2eLS4uhoJfpJxp+OCkNTUJilTIYs9L/fbrruh51Jzt1LqMGTmmHQ9vONGd1sA9NYyqgML9sWepYK+hjyoMpto+bb/8cYwe6G5xGxsbgWE2m00bDod2cXFhy8vLNh6PQ02QSQrqY0BDu6ftb2psU5CH25PwfxphIY/ppZibMrDUfHocUuVfn8d13lPJ/8rsEaCKFkiVX6FUoChrO7QfGr1RLpeD8QIhBQFVC+Zj5Ca9aXl5OQie5MnrDojUdVtbW7Orqyszs2A413pHOnex3aC8kpKHB34evOLLmIFzWqdKPYe0ifYgNKtHuUjg3bq2dd74Zo7U+KdjggAPLzO7E8o1HQ7wOKJ45nFOr2EdeSU6TwCNOX0YT8UTBW1figZ4mJYe+wimz4WlpaWMV17fowW86YsqaarEa+QUSh+7qyGDViqVYJRFqFbDhlk83YzrlG/r1r4ans+aJ3qGFP2Li4vgOEBeYh2maLy+N/afrhu/1vy1GDX1vUUChg7mE9B6WkqT9FvnUXmIN8AAMeOcWdbTHeOTfm6VVqZ4kl4Te27sXtavKvSsDWQwDEBq2DCzYLRnPLyhCsBBUGS9KOgpazg1DqkxVEjJEn7cMFSgb1CgtlarWaVSCbwPvFejMc/xBkB1IkDDS6VSMNITFc480Rfl92pYV4eRpk/pdSm9mnHwTiz68hRQqVSCvoi8QRQpMsmLFy/s/Pzcbm5uQkQMfUJ+5vpKpWLffPONvXr1yhqNhq2vrwdaqnX6UoYZTzvz+BAyEGnjlUolWjtpGphG9p9Gf/HX67pWQ2DefE401JjdF95iDQXyBiH2n1f6MdSw84qGnqoHI9YWHQDPoPz7uYYQdPZ095bNSX2KjcE016QU6acw1Kh3bVJbvREgdb0SGiyjz549s2+++cY2Njas0WjY9vZ2UDjW1tbsw4cPgYm8efPGRqNPBVs17WxawVChaGV6WpiEkEUbasymG5NpDB2AEgo9xkizvLwcjJgw95iQlmdMYE2r8qnepGkhZgDw/VIFxgttCNmNRiPgRL1eD/Vo8JSTJqIKZFGg6SAAY+7f5Ql6bCweYniZZqxT1+Q9U5U6r+Dr9X7+vGAdW6exe1Nt8PepYlakQEpEC+sKQ5AqjawvlD+zOyGCsSFdiSgDlDc8gwgbHz9+tIWFBavX6+H8ysqKmd2FJtNXpbc3Nzf28ePHcA6Pqo6Pn28V+hk37zGkX15B9XJCzFBjZqHw6mg0CvXMiBwjgmZubi7UtlpaWgqRBkUCqWtEu6gxRtOutRYNkVSqqDOWGu3FnGpEiY5Pai17HOJ6Hz3GOzQiTVMjdF5iQm5ee3RdeIVGU95ic+7xXhUWFciLAsaYCEMgJugzDj4ywrdHcQinE3VqFN8BdTroeOt5b7BirMyyheExiLCLGDhMmgHvow28L6WEeN4xaa3Rdk+XdX17R2lRgKGG92k0FO+OGTV1HabSpFi/3ujsI4z0Od4gyjvzjKAxPPPn/TM5rw4wxRN4ycePH0P65e3tbahrdn19HXjPx48f70Vacp6IOCIDWctFATxGDWneMZCnl3l5xhsOOWZuGVdSayqVSihKS7FvdEaNBvTrSNvAMzGoqGFFa6tonTVdT6qf6lrTsSZyBOMPY+INyuPxXUSRGr80AtSPTxFQqVTCLmxsHrK0tGTPnz8Pm298++23dnZ2Zh8/frTNzc0Q6VOv163b7dpoNLIXL14EB9Pf/d3fBUcTdfegI54+MqaxdTLJ3sC4U18MnV5xa5r3+GemZPBJMEmXVv6aR1NLD1GUZjCDGcxgBjOYwQxmMIMZzGAGM5jBDGbwdPB0ZdxnMIMZzGAGM5jBDGYwgxnMYAYzmMEMZvAgmBlqZjCDGcxgBjOYwQxmMIMZzGAGM5jBDP5GYGaomcEMZjCDGcxgBjOYwQxmMIMZzGAGM/gbgZmhZgYzmMEMZjCDGcxgBjOYwQxmMIMZzOBvBGaGmhnMYAYzmMEMZjCDGcxgBjOYwQxmMIO/EZgZamYwgxnMYAYzmMEMZjCDGcxgBjOYwQz+RuD/AEXjSdxXFl7oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_size = 10\n",
    "test_latent = torch.randn(test_size, latent_size).to(device)\n",
    "\n",
    "test_images = gen_net(test_latent)\n",
    "\n",
    "fig, axs = plt.subplots(1, test_size, figsize=(20, 4))\n",
    "\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.axis(\"off\")\n",
    "    ax.imshow(test_images[i, 0].cpu().data.numpy(), cmap=\"gray\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
